The total in-car navigation market has been in continual decline for the last three years, but ABI Research believes it has now reached its lowest ebb. While pure navigation is unlikely to reach the highs of 2008 again, the overall market is reaching a revenue plateau, creating a solid platform on which connected in-car services can bring a new generation of revenue growth, the market research firm concluded.
Senior analyst Patrick Connolly stated,” When we look at the decline from 2008 to 2011, there is a perfect storm of economic conditions, low-cost/free smartphone navigation, the decline of PNDs, and falling car sales. The market is forecast to reach a low of $22 billion this year, before fluctuating around the $22-$24 billion mark, as a new period of growth for factory-fitted solutions, coupled with smartphone solutions, will take in-car navigation towards saturation point in many regions by 2017.”
Factory-fitted solutions will bring new revenue opportunities, especially for PND manufacturers, ABI Research said. But the real growth opportunity will be the additional revenues that in-car connectivity will bring. Companies are fighting for a near-30 million connected car platform market in 2017, with many of the winners and losers decided over the next two years.
Practice director Dominique Bonte added, “The opportunity is there to leverage navigation, to bring a host of new services around driver performance, infotainment, car diagnostics, and insurance.”
These findings are part of ABI Research’s GPS & GNSS Research Service, which includes additional Competitive Analyses, Vendor Matrices, Market Data, and Insights. In ABI Research’s quarterly service, “GPS&GNSS”, all forms of in-car navigation are considered, including factory fitted, aftermarket, PNDs, and smartphones.
Rand McNally has announced that its mobile communication systems are now certified and integrated with two applications from TMW Systems, Inc. As a result, fleets that use TMWSuite or TL2000 now can pull data from Rand McNally’s TND 760 and TruckPC in-cab devices through their TMW products.
“Rand McNally has been a TMW Business Alliance Partner for decades,” said Scott Vanselous, CMO of TMW Systems. “By certifying integration with Rand McNally’s mobile communication and management systems, our customers have ready access to a full suite of Rand McNally solutions.”
One customer is Freight Exchange of North America (F/X), a Chicago-based, North American full truckload carrier that operates nearly 300 power units from its terminals in Southern California, El Paso, Dallas, and Chicago. F/X has integrated information from Rand McNally’s TND 760 (Fleet Edition) with TMWSuite. For F/X, the integration allows for the use of real-time information from the in-cab device to dispatch trucks, receive automatic arrival and departure notification, match loads with available drivers, and track the progress of the driver’s daily workflow.
“TMWSuite has been a tremendously successful tool to manage our operation. Rand McNally’s integration allows us to leverage our investment even further,” said Fred Alaimo, V.P. of Operations at F/X. “The TND 760 offered more functionality than other solutions we reviewed, and it’s priced significantly more competitively. The icing on the cake is that the drivers love the new device and have been quick to adopt and use the technology, making everybody a winner.”
The TMW certified integration pulls critical data from Rand McNally’s in-cab systems via Rand McNally Connect software. The data provided by Rand McNally’s devices enable TMW products to deliver the following:
Automated and standardized driver daily workflows;
Notification of vehicle arrival and departure via Rand McNally’s automated geofencing capability;
Automatically linked information — such as bill of lading numbers — from one form to another further simplifying the driver experience;
Integrated Hours of Service information into load planning and dispatching operations.
“Having a certified solution with TMW’s industry-leading enterprise transportation management systems enables customers to confidently integrate the benefits of mobile communication, award winning navigation and fleet management,” explained Dave Muscatel, CEO of Rand McNally. “In particular, our TND 760 Fleet Edition device offers fast ROI recognition due to its cost effectiveness, ease of use and driver acceptance.”
By Hans-Georg Büsing, Ulrich Haak, and Peter Hecker
Future safety-relevant driver assistant systems demand vehicle state estimations accurate enough to match the position within a road lane, which cannot be provided by standalone GPS. A promising approach to meet the requirements is the fusion of standalone or differential GNSS measurements with vehicle sensor data like odometers or accelerometers. To achieve deeper sensor integration, a software GNSS receiver was developed at the Institute of Flight Guidance (IFF) that is able to use dead reckoning sensors to support its signal acquisition. This article presents an approach to estimate the signal states during outages based on the tightly coupled vehicle state, which reduces the reacquisition time and significantly increases the signal availability.
GNSS-based navigation is a key enabler for future advanced driver assistance systems (ADAS). Car manufacturers have identified automotive assistance systems as core devices to propose their uniqueness mainly in the luxury and upper-class market segments. While the precision and availability of loosely coupled single-frequency GPS navigation satisfies the requirements of typical route guidance systems, future automotive systems — especially those that enhance driving safety — are more demanding on positioning system performance.
The Institute of Flight Guidance (IFF) of the Technische Universität, Braunschweig, Germany, is involved in two research projects evaluating the performance of unaided traditional GNSS receivers coupled with vehicle sensor measurements such as odometers in a tightly coupled architecture. Besides these involvements, the IFF has developed a general-purpose software-based GNSS receiver allowing full access to signal processing routines.
The benefits of the tight sensor fusion are reliable state estimations even during total signal outages that are common in the automotive sector due to tunnels, parking decks, or urban canyons. In this architecture, the GNSS receiver works autonomously to deliver raw GNSS-measurements only. Additional knowledge provided by the vehicle sensors cannot be used to support the receiver in any way. Besides other beneficial aspects in the tracking channels, additional external knowledge about the vehicle state has the potential to reduce acquisition times and improve the measurement availability significantly.
The Institute of Flight Guidance uses a software environment called “Automotive Data and Time-Triggered Framework” (ADTF) for research in the field of ADAS and automotive navigation. In this software framework, the overall system architecture is assembled with independent modules. These modules are implemented as libraries and loaded into ADTF. Data is exchanged via pins that are defined as public variables. The framework also attaches timestamps to the individual measurements and adds a data recording and playback functionality.
From a general-purpose software GNSS receiver, presented at the ION GNSS 2010, we have derived an automotive-specific ADTF software receiver module. The software framework adds the flexibility to synchronously process measurements from vehicle sensors additionally to the IF data from the front end. This gives us the opportunity to aid signal processing in the software GNSS receiver with additional external sensors.
For positioning, a tightly coupled positioning filter based on GPS raw data measurements and the rear-wheel odometers is implemented. The vehicle’s motion is modeled using a kinematic relationship between the vehicle sensors and the GNSS measurements.
Based on the tightly coupled vehicle state estimation, an acquisition state is processed during signal outages that enables the software GNSS receiver to reacquire the satellite signal instantaneously with high precision.
In this article, the constituent parts of the system are presented and the estimation of the acquisition state derived. The system was tested in an urban scenario, and the state estimations validated with the recorded measurements.
System Architecture
The software-defined GNSS receiver developed by the IFF was designed to process the computationally expensive signal correlation on an Nvidia graphics board using the vast parallel processing capability of graphics processing units (GPUs). With the use of common graphics boards, an entire receiver can be implemented on an ordinary PC, needing only a front-end to receive digital GNSS signals in an intermediate frequency (IF) band.
For research in the field of vehicle state estimation, a derivate of the software receiver of the Institute of Flight Guidance has been implemented in the “Automotive Data and Time-Triggered Framework” (ADTF). The software is commonly used in the automotive industry for the development of ADAS. Figure 1 shows a typical system layout in ADTF. A central component of the framework is the ability to record and play back measurement data, which is indicated by the buttons on the left of the screenshot.
Figure 1. System Architecture in ADTF. (Click to enlarge.)
Within ADTF, the systems are assembled from modules that are shown as blocks within the graphical configuration editor. Standard modules such as the connection of common hardware are provided with the framework. Custom modules can be implemented in C++ by the user. Every module is implemented as a dynamic library (DLL) and interpreted by the framework. Modules can be featured with input and output pins.
These pins are implemented by using specific data types from the framework. The communication and data exchange between the modules is handled via these pins. They can be connected by graphically drawing connector lines in the configuration editor.
ADTF provides the user with classes for timing and threading. Processes can thereby be linked to the ADTF system time, which is especially important as the data replay can be slowed down or sped up for debugging.
The instantaneous reacquisition algorithm is based on a traditional approach of tightly coupling GNSS raw data with vehicle sensor measurements. The fusion is based on a kinematic model following the Ackermann geometry establishing the relationship between the vehicle’s motion and the respective measurements.
At each time step of an arriving measurement, the vehicle’s motion is predicted based on the last estimated state with an extended Kalman filter. The prediction is then corrected using either measurements from the vehicle sensors or GNSS raw measurements. The range and Doppler measurements are calculated in the tracking channels of the ADTF software GNSS receiver. The corrected vehicle state is then fed back into the kinematic model for the next update cycle.
In case the GNSS signal is lost in a tracking channel, a virtual tracking channel is initialized with the last calculated channel states. The change in the channel output is then predicted utilizing the change in the vehicle state and the current evaluation of the ephemeris. The schematic implementation of the channel state prediction is shown in Figure 2.
Figure 2. Schematic of Channel State Prediction. (Click to enlarge.)
Signal State Estimation
Using the tightly coupled architecture presented above, an estimated position and velocity can even be provided during total signal outages. Assuming that the last valid observation of a satellite signal is stored together with its respective time to and position, an estimation of the signal state (that is, Doppler frequency, code- and carrier-phase) based on the estimation of the vehicle state during the signal outage at time t1 can be used for an instantaneous signal reacquisition. Using the ephemeris data provided by the respective GPS satellite the range between a user position xu and the satellite xsv can be calculated using the following terms (1)
and (2)
with |…| indicating the Euclidian distance.
Therefore the change of the range can be obtained with equations (1) and (2): (3)
Assuming an unbiased Gaussian error distribution of the measurements, the tightly coupled system provides an estimation of the covariance matrix of the vehicle state. Using only the submatrix (4)
related to the vehicle position, the covariance of the user position along the line-of-sight to the satellite can be obtained with the Euclidean norm of the line-of-sight vector (5)
and the law of error propagation: (6)
Furthermore, using the law of error propagation, it can be shown that the variance of the change of range estimation in equation (3) is obtained by: (7)
With the last valid range measurement related to time to, the signal state at time t1 can be obtained for the pseudo-range PSR (8)
and the carrier phase Φ: (9)
The resulting variance of these estimations can by expressed by (10)
and (11)
respectively. The estimate of the Doppler and the related variance can be obtained analogous.
Considering the variances of the estimation, it can be decided if the signal can be reacquired instantaneously or if the receiver has to find the signal using standard acquisition routines in a limited search space.
Experimental Validation
The Volkswagen Passat station wagon operated by the Institute of Flight Guidance was used to evaluate the performance of the proposed algorithm (see PHOTO.) The test vehicle is customized from the standard by adding an additional generator to meet the power requirements of the measurement and processing hardware. In addition, the Controller Area Network (CAN) is mirrored and open to access the data collected by the sensors of the vehicle. The relevant sensors include a longitudinal accelerometer, a gyro for measuring the yaw rate as well as the odometers of all four wheels. The test vehicle is equipped with a GNSS front-end developed by the Fraunhofer Institute for Integrated Circuits. It is capable of streaming L1, L2, and L5 RF samples via two USB ports. The sampling rate of L1 is 40.96 MHz at an intermediate frequency of 12.82 MHz.
Test Vehicle. A customized Volkswagen Passat was used to evaluate performance of the algorithm.
The vehicle sensor data is streamed via CAN to an automotive PC from Spectra. It is equipped with an Intel quadcore CPU, 8 GB RAM, a Vector PCI CAN device and 256 GB SATA solid state disk allowing up to 195 MB/s writing speed. Additionally, it has been equipped with an Nvidia GeForce GT 440 graphics board that is used for processing the GNSS RF data. This specific graphics board was chosen because it offers a comparably high performance of the GPU at relatively low power consumption.
Both GNSS RF data and data from the vehicle sensor network are streamed to an ADTF hard disk recorder. Due to the setup of the data acquisition, several challenges have to be solved. The first challenge is that the front-end needs to be used as hardware-in-the-loop. It is by itself not equipped with an automated gain control. Therefore, it is not possible to just stream the RF data but it has to be decoded, processed for adjusting the gain, and then stored to the hard drive.
Secondly, the recording setup needs to cover high data rates. The GNSS front-end streams approximately 20 MB/s. As the data needs to be decoded and processed for gain control, the expanded data rate for recording is ~40 MB/s. In total including vehicle sensor measurements, >2000 data packets per second are streamed to the recorder. Because this could not be done using mechanical hard drives, we used solid state disks that also allow data storage during times of high vibration.
Related to the before-mentioned challenges, an efficient thread management needed to be implemented. The software framework’s threading classes are utilized to parallelize the receiver processes. Additionally, it has arisen that a significant part of the processing time is taken by the data transfer to the memory of the GPU.
In order to prove the advantages of an odometer-aided reacquisition, an applicable testing scenario was chosen. To distinguish an odometer-based aquisition approach from a model-based approach, a trajectory was chosen that features a right turn of 90 degrees immediately after cutting off the GNSS signal. A model-based kinematic prediction would project the trajectory in the direction of the latest known heading derived by the GNSS solution. Only a sensor-based state estimation is able to resolve the right turn. The driven trajectory is shown in Figure 3.
The GNSS signal has been cut off for approximately 10 seconds, which is equivalent of a 75-meter drive on dead reckoning sensors only after the right turn.
Figure 3. Trajectory of test drive includes a 90-degree turn. (Click to enlarge.)
Results
The following plots in Figure 4 show the performance of the virtual tracking channels. The plots in the upper row show the pseudorange output over time. For vividness they have been corrected for the motion of the respective satellite that is dominant due to their high speeds. Over a short period of time the satellites’ motion relative to the receiver can be linearly approximated. The pseudorange measurements over time were fit using a linear regression. The respective value of the linear regression was then subtracted from the pseudorange and plot over time as shown in the figures in the second row, leaving only the approximated influence of the vehicle’s motion.
Figure 4. Modified pseudorange and Doppler results of the virtual tracking channels. (Click to enlarge.)
The Doppler measurements have been similarly compensated by just subtracting the minimum measurement. These modifications of the pseudorange and Doppler measurements allow a direct comparison of each other as the Doppler can be understood as the first derivate of the pseudorange over time.
The results of PRN 6 show that the Doppler estimate during the GPS outage smoothly fits into the surrounding measurements without any major outliers. The plot of the pseudorange shows a similar behavior. The pseudorange could have potentially been modeled using a dynamic prediction that is not based on vehicle sensors due to the limited dynamics on the pseudorange measurements.
The Doppler plot of PRN 16 shows a strong change in the relative velocity between satellite and receiver. If a further projection of the Doppler using a linear dynamic model would have been used instead of predicting with vehicle sensors, it would likely have misled the reacquisition by ~ 50 Hz. The trend in the pseudorange measurements is comparable to PRN 6 at a higher rate of change.
The plots of PRN 21 probably show the advantages of using vehicle sensors for reacquisition best as the dynamics on pseudorange and Doppler are the most significant in the group. Both pseudorange and Doppler show a turning point during the GNSS outage. Especially, the pseudorange would have been mismodeled using a kinematic predicion that is not relying on additional sensors.
Conclusion
In this article, a tightly coupled positioning system implemented in the automotive-specific framework ADTF was presented that is based on the fusion of standard automotive sensor data and software receiver measurements. We showed that, using the tightly coupled solution, an acquisition state during signal outages can be estimated that allows the tracking channels to reacquire the signal instantaneously without the need of computationally expensive acquisition routines.
Under the assumption of a tightly coupled RTK position and small outage times, a reacquisition of the carrier phase without loosing the information about the phase ambiguity seems possible.
In the next version of the automotive GNSS receiver, the authors are planning to integrate the vehicle sensors to aid the tracking loops, which is likely to further improve tracking continuity especially in scenarios with high vegetation. Additionally, we plan to show that the implementation is capable of working in real time. Improvements of the initialization of the virtual tracking loops are also intended.
Acknowledgments
This article is based on a paper presented at ION-GNSS 2011, held September 19–23 in Portland, Oregon.
This work was funded by the Federal State of Lower Saxony, Germany. Project: Galileo – Laboratory for the research airport Braunschweig.
The authors would like to thank their colleagues working in the automotive navigation group for continuous support with the ADTF framework.
Hans-Georg Büsing holds a Dipl.-Ing. in aerospace engineering from the Technische Universität Braunschweig and has been a research engineer at IFF since 2008. He works in the area of applied satellite navigation, especially in the field of vehicle positioning.
Ulrich Haak holds a Dipl.-Ing. in mechanical engineering from the Technische Universität Braunschweig and joined IFF in 2008 as a research engineer. He works in the areas of receiver design and positioning algorithms.
Peter Hecker joined IFF in 1989 as research scientist. Initial focus of his scientific work was in the field of automated situation assessment for flight guidance. From 2000 until 2005, he was head of the DLR Pilot Assistance department. Since April 2005, he has been director of IFF. He is managing research activities in the areas of air/ground cooperative air traffic management, airborne measurement technologies and services, satellite navigation, human factors in aviation, and safety in air transport systems.
Consider two notable developments in 2011 that will influence the development of consumer transportation:
China became the largest manufacturer of automobiles, producing more than 18 million vehicles, easily overtaking Europe and North America.
Smartphone volume shipments surpassed the volume of laptops and desktop PCs combined.
Reflecting these two rising economic rockets, the November Munich Telematics show drew its largest attendance yet, 500-plus participants, and a greatly expanded exhibit area.
The rising dominance of smartphones — one participant observed that they are taking over the world —will have a big impact on how users expect to access or view their telematics data; that is, any wireless information accessed by them while in their car. Developers and manufactures used to have a problem regarding which system to support, but with Android now at more than 50 percent of smartphones share, it is becoming the de facto first-choice standard and will probably become the user interface model.
eCall. Also in 2011, the European Union finally mandated eCall, the emergency call system in automobiles that sends vehicle position to emergency services after a crash. Unfortunately, the mandate is for 2015. I guess this gives them a chance to use the European satnav system Galileo, which hopefully may have something to offer hopefully by then.
This year the Russians leapfrogged the Western Europeans and mandated their own version of eCall, known as ERA, for 2013. It will use GLONASS, the Russian satnav system, which unlike Galileo is operational now. Of course, GPS is still employed, and the real benefit today is using GLONASS plus GPS in a multi-constellation fix mode for higher reliability especially in urban areas compared to GPS alone.
Emergency call in progress, triggered by SOS button in PSA Peugeot Citroen’s roof panel (bottom photo).
At the Munich Telematics show it was clear that the Russian mandate has put wind into the telematics emergency call market’s sails. From the Russian company Cesar’s presentation, we learned that following road accidents in Russia, 14 percent of car occupants die, compared to 2 percent in the United States. Getting emergency support to the scene more quickly is critical to reducing fatalities, and on this basis Russia has got some catching up to do.
You would think that everyone would be rushing to get more safety, and as one market research presenter said, it comes high on the user wish list. Another presenter stated that while people may desire it, they seem reluctant to pay for it at first. As an historical example, initially when people had the option of paying for airbags as an extra, it was practically never taken as an option. Now it is standard in all cars for drivers and passengers.Think about it — would you now buy a car without an airbag?
PSA Peugeot Citroen, the big French car company, shows the way with a version of eCall in their cars that doesn’t lose money! There is a big debate about who gets called when a crash happens. Is it the public service access points (PSAPs) or third-party services (TPS). Peugeot favours the TPS model, which can filter the more common breakdown and false alarms from true crash calls to be forwarded to the emergency services at PSAPs. While eCall initially favoured PSAP, the trend seems to support Peugeot’s decision and TPS.
The PSA eCall also does not support the so-called in-band modem, which allows crash-position data to be sent over a voice call on the eCall box by encoding the data into a speech-like signal. The modem theory is, you need to keep the voice call open to keep talking to the person in the automobile. According to PSA, apart from the issue of patents with the in-band modem, it seems that 30 percent of the data is lost, and 40 percent of the PSAPs in Germany cannot handle it.
GPRS is the best way of sending crash-position data with SMS text message as a back-up. As for voice, most people get out of their car after an accident and do not speak on the eCall box. I guess if people are unconscious and are not able to get out of the car, they won’t speak either.
While smartphones dominate in many areas, they have been ruled out for eCall safety apps in cars, as no one can guarantee a smartphone will work after an accident. As for crash detection, that can only work if a device is bolted down to the car frame. Only that way can you sense the high-G forces during a crash.
Insurance. Until the mandates kick in for eCall/ERA, you can understand why an automobile manufacturer’s marketing imagery does not include one of their car crashing or breaking down. So selling the eCall feature in this mindset is hard. On the other side are guys that do have the image of helping you after a crash: the insurance companies. And true to form, the big business has become insurance telematics.
Octo Telematics has taken a pole position in this area and had an impressive crashing-car demo that you could sit in at the show. The insurance telematics box then becomes an aftermarket product that is cross-subsidized by the insurance company. In return they receive crash data and get to monitor you to help you improve driving habits to reduce crashes.
Octo Telematics crash simulator. Show attendees were taken for a ride! The telematics box sends crash data to the insurance company to help drivers improve driving habits.
A last word on safety: most accidents now seem to occur when people are texting while driving. Apparently when the Blackberry message service was down for three days in Dubai, there were 20 percent fewer accidents.
Apart from eCall and insurance telematics, the other famous perennial telematic application is the connected car. As we all expected, we saw a lot of presentations on this. In simple terms, via telematics, a car is connected to the Internet. As the definition of telematics The branch of information technology that deals with long-distance transmission of computerized information, this might seem a no-brainer. But exactly how the car is connected and what value that offers constitute the two key questions for any application and market segment. Today a car buyer will almost certainly be an internet user.
How Is It Connected? For basic telematic apps like eCall and stolen vehicle recovery, it suffices to connect to the 2G GSM/GPRS wireless network that gives worldwide coverage. Operators like Telenor offer a so called global subscriber identity module (SIM) model that supports worldwide access at a price that makes business real.
For the so-called infotainment connectivity, the trend is 4G LTE, which offers the high data rates that the car companies dream about and flat-rate smartphone users expect. LTE is a packet mobile phone network already at Verizon and in European trial that is ideal for data. It appears that in the future, the best mobile phone network will be a combo of 4G LTE for infotainment data with 2G GSM for speech and 2G GPRS for global coverage telematic data.
What Value Does It Offer? The blanket answer is, unless it offers a useful service, it won’t really be used. Today most connected car services drop to a poor 10–20 percent retention after the free trial period. The key is really to look for helpful services. For instance, the connected heater or rather the ability to switch your car heater remotely on in cold winters of Sweden increased Volvo connected usage 50 percent. Saving fuel in this energy conscious low CO2 emission days would seem a useful application. Couple that with a connected car, traffic information, best routes, good driving-habit rewards, social network to let you post your good driving score, and ….
Fiat showed its eco:Drive solution, helping people save 6 percent on fuel consumption on average. That’s a start.
At the end of the day, more efficient cars are the answer to that. Getting people to use more efficient small cars for short trips is one of the ideas behind the BMW car-sharing model. Based on the BMW One series and the Minis made by BMW, it offers a service in Munich and Berlin (I have to admit I live in Munich and haven’t tried it yet). When you register, you present your driving license and the service add an RFID. You can use this RFID as a keyless entry into a car share. Of course the cars are connected, and a smartphone app helps you find the next free car. You can pick it up and drop it off where you want. Because they are new, more efficient small cars than your average old gas guzzler, they have done a deal to get free parking in town. It costs a flat 29 cents (Euro cents) per minute to drive, which includes the fuel price. I can remember when a mobile phone call cost that much before!
Moni Malek is CEO of ML-C MobileLocation-Company GmbH, based in Munich, Germany.
By Roland Bauernfeind, Thomas Kraus, Dominik Dötterböck, Bernd Eissfeller, Erwin Loehnert, and Elmar Wittmann
Open-field tests of jamming signals from widely available in-car jammers, measured with an experimental software receiver that records the intermediate frequency (IF) samples, enable a detailed analysis of interference effects from these looming threats.
In-car GNSS jammers, openly advertised online as personal protection devices, constitute the most serious threat of all the GNSS interference sources. Such jammers are relatively easy to purchase from abroad over the Internet and to operate by plugging into the cigarette lighter of a vehicle.
Their usage may be motivated by criminal intention such as disabling a vehicle theft-protection system, a fraud attempt against a distance-based road-user charging system or distance-based vehicle insurance, or by privacy concerns, to escape monitoring by a fleet-management or other tracking system. Since most current GNSS receivers carry a communication link, it is difficult to keep full control of the data flow. Further concerns arise from reports of companies storing user location data, as was the case with Apple. Concerns about privacy issues will grow with the widespread introduction of intelligent transport systems (ITSs), vehicles and transport infrastructure that apply information and communications technology to improve transportation efficiency, sustainability, and safety. The primary information source is GNSS for location enabled applications like eCall, a pan-European location based emergency call, which shall be in place and installed in every new car from 2015 on.
Cooperative ITSs, which are currently undergoing standardization, are transport systems that communicate their positions such that each vehicle has a virtual picture of the real world in its vicinity. The cooperative ITS network connects the vehicles with the transportation infrastructure. Vehicles establish a wireless vehicular ad-hoc network (VANET), based on their geographical position. In a VANET the position is communicated to be used at the application layer but is also required at the physical layer to enable geographical routing and addressing. This emerging vehicular communication is an enabling technology many novel and innovative driver assistance systems and location-based services. The result of using an in-car jammer is the complete destruction of GNSS signals not only in the vehicle it is operated in, but also within vehicles in the vicinity. This creates a serious threat to ITS’ future.
To counter the interference threat by in-car jammers, the University of Federal Armed Forces (FAF) Munich purchased some jammers offered online, for analysis in a laboratory environment and in open-field tests in the GAlileo TEst range (GATE). Measurements were taken with an experimental software receiver developed at the Institute of Space Technology and Space Applications. This receiver enables recording of intermediate frequency (IF) samples and detailed analysis of the interference effects on the receiver.
Jammer Interference Signals
First, we analyzed the purchased jammers shown in the Opening Photo. It is always better to understand the signal structure of undesired signals well, before starting development of applicable countermeasures and mitigation technologies. Therefore, the jammers were analyzed in the frequency domain with a spectrum analyzer, and the analyses were extended by a time-domain analysis by recording the signal with a software radio-defined card.
The first results showed that the majority of low-cost in-car jammers transmit a chirp signal with a bandwidth between 9.4 to 44.9 MHz in the E1/L1 band (other frequency bands haven’t been considered yet). The others are sine-wave oscillators with a 3-dB bandwidth of around 0.92 kHz and have a temperature-dependent center frequency around the Galileo/GPS center frequency, but they are not considered further in this article. Both jammer types belong to the category of narrowband interference, however the chirp jammers are much more effective in jamming the signal within the GNSS receivers.
The construction of an in-car jammer chirp signal is usually done by a voltage controlled oscillator (VCO) with an input voltage of a saw-tooth function. In general, it is a sine function with a frequency change over time, which can be described by
(1)
For a unidirectional linear chirp signal the instantaneous frequency f(t) varies linearly over time as
(2)
where f0 is the starting frequency and k is the chirp rate. The amplitude a(t) is usually constant. The corresponding time domain function for a sinusoidal unidirectional linear chirp is
. (3)
All in-car chirp jammers are linear with a positive uni- or bidirectional sweep. The negative slope is so high that we can neglect them for modeling and can describe jammer 1 with the equation (3)
. (4)
Tsw = sweep time.
The frequency spectrum of jammer 1 and jammer 3 is given in Figure 1 and Figure 4, respectively, where we can extract the bandwidth and the peak power from the graph. For measuring the peak power of the jammer it is important to take the max-function mode of the spectrum analyzer, because the internal sweep of the jammer and the spectrum analyzer is never synchronized. Table 1 shows the important parameters of the jammers.
Table 1. Chirp jammer parameters.Figure 1. Power spectrum of jammer No. 1.
To get the timing information of the signal, the analysis must be done in the time-domain. Therefore, we converted the jammer signal into an intermediate frequency and recorded the signal with a SDR card. The further processing has been done with Matlab, where we could extract the frequency change over time for jammers 1, 2, and 3, given in Figure 2, Figure 3, and Figure 5, respectively. Finally, these functions are exactly the same, which were generated for the VCO within the jammers.
Figure 2. Frequency over time at jammer No. 1.Figure 3. Frequency over time at jammer No. 2.Figure 4. Power spectrum of jammer No. 3.Figure 5. Frequency over time at jammer No. 3.
If we compare the jammers, we can see how the complexity increases from one to the other. For jammer 1, a standard saw-tooth generator with a rising slope has been used only for the input of the VCO. Jammer 2 uses two generators. Compared to jammer 1, a second saw-tooth generator with a falling slope and a four-times longer sweep time is added. In the most complex case, jammer 3, we find four generators in total. Jammer 3 causes a frequency burst every 1.12, 1.35, or 2.28 milliseconds. These frequency bursts can be seen also in the power spectrum in Figure 6.
Interference Tests in GATE
Various static and dynamic interference tests were performed in the Galileo Test Range (GATE) in Berchtes-gaden, Germany, where the impact of the jammer signals on both GPS and Galileo RF signals could be evaluated in a realistic manner. GATE is a unique outdoor test and development environment for Galileo and GPS satellite navigation. Consisting of eight virtual Galileo satellites located atop several mountains around the test area in Berchtesgaden, GATE provides a topology to support different testing scenarios. The Galileo signals are transmitted simultaneously on all three frequencies. E1, E5ab, and E6, compliant to the Galileo OS ICD specification. GATE’s virtual-satellite mode simulates a realistic moving Galileo satellite constellation and supports commercial Galileo receivers without any modification. Two monitoring stations within the test area receive and process these signals. A central processing facility steers and controls the signals transmitted.
Figure 6 gives an overview of the test range with its transmit and monitoring stations as well as the GATE central point. The interference tests with the GNSS jammers were performed in the area close to this central point.
With respect to the testing of RF jamming scenarios including GPS as well as real over-the-air Galileo signals in the GATE test area, some requirements have to be taken into account.
Transmission of any interference signals on the GPS and Galileo frequency bands requires an official license from the responsible authority in Germany (Bundesnetzagentur). An appropriate permission for trial radio transmission was available in the framework of the jamming tests. The disturbance of other GPS receivers in the vicinity has to be minimized in any case. Therefore the transmission power of the jammers must be limited so that a distinct impact on the GPS L1 signal reception is restricted to a radius of a few hundred meters at the most. Furthermore, the interference signal source must be placed at an adequate distance from the GATE monitoring station antennas in order not to affect the processing and steering process for the GATE signals.
Finally, in the case of performing GATE tests with a dynamic test user receiver, a severe degradation of the user reference position must be avoided. As the steering of GATE signals in the virtual-satellite mode is based on accurate and reliable user position information transferred in near-real-time to the GATE processing facility. a combined GPS-RTK and inertial measurement unit (IMU) solution is applied. Thanks to the use of the IMU, a GPS signal outage can be well compensated for a certain time period. In order to meet the GATE accuracy requirements, the jammer transmission was restricted to time intervals of about 30 seconds.
Ipex Software Receiver
The Institute of Space Technology and Applications PC-based Experimental Software Receiver (ipexSR) is a multi-frequency GNSS receiver realized completely in software (Visual C++/assembler), capable of tracking GPS and other GNSS signals in real time or post-processing.
For signal analysis, IF samples were recorded and analyzed in post-processing, using two front ends that can be operated in different modes depending on required frequency bands. For the interference analysis, only L1 was recorded with the front end parameters summarized in Table 2.
Table 2. Front-end parameters.
The front-end gain is set once for the measurement in the receiver’s configuration menu. The front end uses no automatic gain control. All the tracking loops settings can be set in the receiver’s configuration menu. For the phase lock loop (PLL), we used a non-coherent (Costas) dot-product discriminator and for the delay lock loop (DLL) an early-minus-late discriminator with the settings in Table 3.
Table 3. Tracking loop settings.
Jammer Effect on Receiver
To analyze the interference effect on the receiver, we took measurements with static receivers and different jammers approaching the receivers, starting from a distance of 1,200 meters. Both commercial receivers, capable of recording the carrier-to-noise density ratio, and the Ipex software receiver, capable of recording IF samples, were set up. Receiver antennas were mounted on the car roof. For jammer reference trajectory, we used an odometer with a GPS receiver providing initial position and reference time.
A measurement for the degradation in the receiver is the carrier-to-noise density ratio. The theoretical effective carrier-to-noise density ratio is defined as
where Q is the spectral separation gain adjustment factor. While moving the jammer towards the receivers, the received interference power Preceived(r) increases relative the distance according to the free-space loss as
where Pjammer is the jammer transmission power. Figures 7 to 10 give the C/N0 degradation for the four different receivers interfered with by the three different jammers in respect to the distance. The measurements have been taken at different times so the undisturbed C/N0 is varying.
Figure 7. Carrier-to-noise ratio for IpexSR.Figure 8. Carrier-to-noise density ratio for BeeLine receiver.Figure 9.Carrier-to-noise density ratio for NAVILoc receiver.Figure 10. Carrier-to-noise density ratio for Garmin receiver.
Comparing the professional receivers with professional antenna to the mass-market receivers with patch antenna, it is evident that the professional receivers are interfered with at a later point but lose lock on the signal earlier.
The degradation of the C/N0 for ipexSR compared with the theoretical curve as introduced before is given in Figure 11. The measured curves follow the theoretical one as long as the front end is not saturated. As soon as the front-end analog-to-digital converter (ADC) is saturated, it causes severe degradation of the signal which exceeds the pure degradation caused by the increased interference power until loss of lock on the signal.
Figure 11. Carrier-to-noise ratio for IpexSR (Jammer 1).
Saturation is caused because the amplitude of the received interference power exceeds the range of the ADC. The comparison between the theoretical and actual received signal strength in respect of distance for the measurements of jammer 1 is shown in Figure 12. With an effective jammer transmission power of –40 dBW, the curves show good alignment for the interval where the received interference power is noticeable above the noise floor, until the front
end goes into saturation and the received signal strength converges to an upper limit.
Figure 12. Received signal strength (Jammer 1).Figure 13. Sample distribution over 8-bit ADC (Jammer 1).
The rising received interference power drives the IF samples to the outer limit of the ADC and changes the distribution of the IF samples over the bins of the ADC as shown in Figure 13. For these measurements, the gain of the front end was set to have the samples without interference distributed over all the ADC bins. This setting with low remaining dynamic range is optimal when no interference is present, whereas with interference the ADC goes immediately into saturation. The red line shows the distribution of the samples where 0.2 percent of the samples are at the outer boundary.
Figure 14. Punctual correlator output (Jammer 1).
Until saturation of the front end, the interference degrades the correlation process by raising the noise floor. When the dynamic range of the front end can no longer occupy the received interference power, the degradation by saturation dominates. For the undisturbed signal, all the signal power is in the I-channel as seen at the punctual correlator output in Figure 14. The correlation is degraded until loss of lock on the PLL occurs.
Degradation of the correlator output has a direct effect on the performance of the tracking loops and their discriminator outputs, as shown in Figure 15. The discriminator error rises until it is out of the discriminator function’s pull-in range. When the PLL error is outside the pull-in range, the tracking loop loses lock on the signal.
Figure 15. DLL and PLL discriminator outputs (Jammer 1).
The degradation of DLL performance causes a position error as shown in Figure 16.
The measurements show that currently available in-car jammers degrade the receiver performance in an radius of about 1 kilometer around the interference source and disable position determination within a radius of about 200 meters.
Interference Detection
Jammers constitute a serious threat to the future of intelligent transport systems. Their use is forbidden by law, and their illegal use must be prosecuted. To have awareness of the actual number of jammers in use requires deploying jammer detectors at dedicated points and recording interference events. Promising points for initial measurements would be highway interchanges or highly frequented border crossings. Reliable numbers on the actual use of GNSS jammers would be required to support government decision-making regarding further actions, and to support the final goal of an comprehensive GNSS interference monitoring network.
For the interference detection test, we recorded were recorded with five static receivers deployed in the GATE core area as shown in Figure 17, with jammer trajectory in red.
Detection of the interference source is based on monitoring the jammer-signal-to-noise ratio (JNR). To prosecute malicious intentional jamming, it is necessary to assign the detected interference signal to the jamming device. Therefore, the signal was analyzed in the time-frequency domain for the characteristic chirp signal of a jammer. The gain of the front end was set to the minimum so that the front end could cover high interference power levels
First, signals were recorded with the chirp jammer located at the central point. The jammer is located outside the car, with line-of-sight to position 1. The measurements at position 1 at about 200 meters from the jammer are shown in Figure 18. Short-time Fourier transformations of the signals in Figure 19 and Figure 20 clearly show the presence of the chirp signal.
Figure 18. JNR at Position 1.Figure 19. STFT of Jammer 1 at Position 1.Figure 20. STFT of Jammer 3 at Position 1.
For the second measurement, the jammer was used inside a car. The car started at position 1, where it switched on the jammer and drove along the main street, passing position 3. The car then turned and drove back the same way. The measured JNR at the five positions is illustrated in Figure 21.
Figure 21. JNR with jammer 1 moving.The resulting degradation in C/N0 is presented for GPS PRN 9 in Figure 22 and for GATE PRN 46 in Figure 23. The measurements show that the jammer can be detected and identified within the distributed receiver network.Figure 22. C/N0 of GPS PRN9 with jammer 1 moving.Figure 23. C/N0 of GATE PRN46 with jammer 1 moving.
The next step in developing a comprehensive interference-monitoring network would be to have automotive GNSS receivers enabled to detect and report interference events. For this scenario, a jammer was operated in a moving car and measurements with the ipexSR driving in another car on the same road were made.
Both cars started at the same position. The pattern in Figure 24 corresponds to the following events. The jammer started first, followed by the receiver with a random car in between. After 170 seconds, the jammer parked at the roadside, and the receiver passed by, indicated by the single spike. At about 240 seconds, the receiver turned and passed by the parked jammer again, as indicated by the second spike at 310 seconds. After the receiver passed by the jammer, the jammer started again, approached the receiver from behind and overtook the receiver at 450 seconds.
During this measurement, neither of the two cars could track or re-acquire a signal. Reporting of the loss of lock on all satellites could therfore be used for a coarse localization of jammers.
Figure 24. JNR in a traffic environment with jammer 1.
Conclusion
The analysis has shown that the interference range of a jammer is very dependent on the receiver architecture. In every scenario, the jammers had severe effects. After detecting interference events, the next step is to mitigate their effect within the receiver. Mitigation techniques based on time-frequency transformations like short-time Fourier transform or wavelet packets are envisaged. With the ipexSR IF Sample API, Figure 25, it is possible to implement and test these algorithms in real time.
Figure 25. IF sample API.
Also the possibility of localizing the interference source based on the JNR and C/N0 measurements will be e
valuated.
Steps against the use of in-car jammers must be taken. To prosecute the use of jammers, detector units must be deployed. This would also help to gather reliable numbers on the use of jammers and would support and justify future actions. Clearly, degrading the integrity of GNSS positioning is a threat for all safety-relevant ITS applications. Therefore, avoidance and mitigation of interference signals should be subject of safety-related vehicular communication, and its standards should be able to handle this in the same way as other safety-related issues. We propose discussion of the GNSS jammer threat within the working groups for cooperative ITS standardization: GNSS interference should be handled in the same way as any other road hazard.
Acknowledgments
These results were developed during the InCarITS Project (Analysis, Detection and Mitigation of In-car GNSS Jammer Interference in Intelligent Transport Systems), founded by the Bundesministerium für Wirtschaft und Technologie and administered by the Project Management Agency for Aeronautics Research of the DLR in Bonn (FKZ 50 NA 1001).
Manufacturers
Jammers were analyzed with a Will’tek 9102B spectrum analyzer and signals recorded with a GE ICS-572B software-defined radio card. The two front ends were developed by Fraunhofer Gesellschaft (FhG). Receivers used for jamming testing were ipexSR with NovAtel GPS-704-X antenna and FhGIII front end, a NovAtel BEELINE with the same antenna, a NAVILock NL-302U Sirf3, and a Garmin GPSMap 76, the latter two both with patch antennae. Only the IpexSR was used for tests to locate jammers, using an FHGIII front end and NovAtel GPS 511 antenna (Position 1, 5), the same antenna with an FHGII front end (Position 2, 3), and an FHGIII front end with SensorSystems S67-1575-96 antenna (Position 4). The two-car driving test used the IpexSR with Novatel GPS-704-X antenna and FHGII front end. IFEN GmbH developed and installed the test range and is GATE operator at least until end of 2013.
Roland Bauernfeind works at the Institute of Space Technology and Space Applications at the University FAF Munich. He received a diploma in aerospace engineering from University of Stuttgart.
Thomas Kraus is a research associate of the Institute of Space Technology and Space Applications at University FAF Munich.
Dominik Dötterböck is a research associate of the Institute. He received his diploma in electrical engineering and information technology from Technical University Munich.
Bernd Eisfeller is director of the Institute of Space Technology and Space Applications at the University FAF Munich. He is responsible for teaching and research in the field of navigation and signal processing.
Erwin Loehnert received a diploma in aerospace engineering in from the Munich University of Technology. He is head of the Mobile Solutions department at IFEN GmbH, and GATE manager.
Elmar Wittman received a Dipl.-Ing. degree in geodesy from the Munich University of Technology. He works as a systems engineer in the field of GPS/Galileo satellite navigation for IFEN GmbH.
Today, some of the most exciting innovations in consumer electronics aren’t the ones in your living room or your office — they’re the ones inside your car. — Audi CEO Rupert Stadler
While most automobile magazines do a great job of reviewing the performance of automobiles and trucks, they do not adequately address the vehicles’ GPS or positioning, navigation, and timing (PNT) capabilities, sensors, or electronics suites. Nor do they endeavor to fully grasp how these sensor suites, many enabled by GPS and other PNT devices, add to their safety, peace of mind, and overall situational awareness. My pick of the best automobile currently on the market for driver situational awareness is the 2011 Audi A8.
Lest you think the choice was easy, it was not. For two years I drove more than 26 different candidate automobiles and I found myself repeatedly comparing them to the A8L. The Audi 8L is designated by its maker to premiere and test all electronic features — hardware and software, including situational awareness devices — that may eventually go into production on other Audi models.
I noticed when I began testing automobiles that, on the high end, they were fairly uniform in performance. The majority of them went from 0 to 60 miles per hour (0 to 100 kilometers per hour) in less than five seconds. They all stopped or went from 60 to 0 in approximately 100 feet (30.48 meters), depending on the tires, weather, and road surface. They were all reasonably quiet and to some degree comfortable. The average fuel mileage varied from 15 to 27 miles per U.S. gallon, with the Audi A8L taking honors in this class. However, the models varied tremendously in their electronic sophistication, integration, and situational awareness: some vehicles kept the driver situationally aware, and some failed miserably at this critical task.
I look not only at the electronics and how they are integrated, but also how easily and completely they inform the driver in all sorts of traffic and weather conditions. Do the windshield wipers activate automatically when it rains or you enter a fog bank? Does the navigation system automatically reroute you or at least offer that option when weather, accidents, or delays are encountered? Does the PNT system alert you in time to take evasive action in a potential dangerous situation? Does it present the mapping interface and alerts so that you are aware of your options both aurally and visually? Do you have to manually intervene or merely follow clear and precise directions?
Every major automobile maker and dealer I spoke with said that the majority of serious buyers today look for performance and style as always — but those have become secondary to the options provided, mainly the electronic awareness, safety, and entertainment suites. Of course, makers and dealers also appreciate the fact that these options, while adding safety, convenience and awareness, also add — often significantly — to the bottom line, or the vehicle’s drive-away price. So, yes, situational awareness does come at a price and sometimes a steep one. However, if it gives you peace of mind, lower stress, and saves lives, it is hard to complain. One can certainly make the argument that all these devices should be available on all automobiles. As time goes by they will be, and at a lower price. For now, we pay a premium for them. But what price can you place on a human life? Rest assured, many of these features are potentially life-saving.
Stealth GPS
I want to alert you to a phenomenon some GPS subject matter experts and I discovered while researching for the Department of Defense. It surprised us, but in retrospect we have always suspected the phenomena existed; we have chosen to call it Stealth GPS.
Stealth GPS exists in many military platforms today, and the practice now extends to the automotive industry as well. Basically, 90 percent of the more than 1 billion GPS users in the world use GPS for time or timing purposes and not for just position or navigational purposes. Obviously, in automobiles with very high-tech systems onboard, timing and synchronization are critical. Since GPS chips today are relatively inexpensive, they occasionally show up in unexpected places. No less than five major auto makers told us that every model they produce has a single and more likely multiple GPS chip(s) embedded somewhere in the electronic suites. These automobiles may or may not have a standalone GPS display, and it may not be obvious to the owner or even the mechanics that work on the vehicle, but GPS information, including timing data, is essential to proper vehicle operation.
For example, on the Audi A8L the Quattro sensors measure tire adhesion or slip up to 100 times per second and report that information through the traction-control system’s electronics. This requires precision timing and a tightly integrated timing or synchronization system.
Consider that GPS time is distributed freely around the world, and relatively cheap quartz crystal clocks can act to hold over precise GPS timing for a considerable period when the vehicle’s GPS antenna, also usually a stealth device, cannot see the sky. GPS chips in addition to position and navigation information may provide time of day to include day, month, year, hour, seconds, and divisions of seconds down to 1 x 10-14, along with altitude, attitude, heading, and velocity information, all independent of any other sensors on the car. As you will see, when GPS data are tightly integrated with other sensor data and display systems, the resulting displays and capabilities can be almost staggering in their versatility and ability to make the driver situationally aware.
How many GPS chips, stealth or otherwise, does the Audi 8L carry? Frankly, I am not sure, and it’s just possible that neither is Audi; after all, some of them are likely very stealthy. But regardless of how many there are, they inform and enable a dizzying array of displays, capabilities, and overall situational awareness second to none.
When I drove the A8L, every time I wanted a piece of information that the situation demanded, it always seemed to be readily available, and usually in more than one location. There is a pop-up full-color 8-inch display screen in the center console and a full color 7-inch display screen directly in front of the driver, between the speedometer and tachometer. The 7-inch screen is so well integrated that until information starts to appear, you never know it exists. I did not have to search or push buttons or pull levers — the information was simply there when I needed it.
The Audi’s displays were the most intuitive I have experienced to date. So much so that after experiencing the Audi’s non-intrusive total situational awareness capabilities, they were subsequently conspicuously absent on any other vehicles I drove.
The Audi A8L is available with all of what Car and Driver calls Audi’s latest “electronannies,” including a multimedia interface (MMI) and voice-controlled GPS display, which disappears when not in use or when the automobile is turned off. There is also active and adaptive cruise control with low-speed stop-and-go capability that will actually initiate and fully stop the vehicle if you are about to collide with an object, person, or another vehicle — and you fail to stop the car yourself.
The A8L has
a blind-spot monitoring system;
a camera-enabled lane-assist mode that turns on above 40 miles per hour and warns you with a steering wheel vibration when you are wandering in your lane or about to intrude on another;
a night-vision system that displays yellow silhouettes for anything warm-blooded ahead, including pedestrians and those lovable but pesky Bambis lurking by the side of the road; when such creatures are directly in the car’s path, the alerts turn bright red.
a visual reverse navigator in the center pop-up that clearly displays the exact parking path the car will take depending on how you turn the wheel. The proximity sensors beep with increasing frequency as you near objects and turn to a solid tone when you are within four inches of the object. I parked the Audi A8L several times solely by monitoring the center display.
While these wonders are merely enabled by GPS, the display screens in the vehicle are nothing short of amazing in their capability and versatility. The touch-screen color display can enable almost any feature of the automobile through a mere touch while many features are MMI- and/or voice-activated. You quickly learn, if your hands are occupied keeping you on the road, that you merely need to speak, and the Audi quickly obeys.
Road Trip
Before driving from Colorado Springs to Denver and back, I spent two very informative hours with the dealer staff going through the A8L’s features and capabilities. They do this with every prospective buyer — a good thing because the number of features can be daunting. But once you are actually driving, everything seems intuitive and, most important, non-distracting. I never once had to hunt for switches or buttons, because if you can’t remember, just use the audio system and tell the Audi what you want or need.
On the open road, I headed north to Denver. I set my destination merely by asking aloud for the Denver airport; the system immediately gave me a choice of the three airports in and around Denver, and I selected one. I could have looked up all airports within 100 miles, or put in the address if I knew it, or just browsed local transportation options, or even input the coordinates if I had them.
The center display always gave me the speed limit of the road I was traveling; it allows you to set a warning if you exceed that speed by your choice of number. The car is so quiet, there are no audible clues as to your actual velocity. If there had been any speed cameras on I-25, the Audi would have warned me about them as well.
The car always displayed the next three turns in blocks that clearly gave the mileage to the turn, the direction and degrees of the turn, and the name of the exit and road to turn onto. A mile before each exit, the navigation system displayed all its amenities and points of interest (POIs): gas stations, motels, hotels, restaurants, hospitals, and cash machines. It can display much more or less, depending on how you program, it, but the logos for the amenities show up just like they do on some road signs with the same information (although the road signs never seem to be there when you need them, or they go by too fast to read). Plus, both the center and driver’s panel displays show in bright vivid blue your route and the turns to make, the lane you should be in, and very accurate distances and times to the next turn, your final destination, and any intermediate points.
Wonder of wonders, when I turned off the prescribed route (on purpose), I never heard the dreaded “Recalculating…” The system adjusted and gave me new data to my destination based on my waywardness, and a pleasant suggestion to “proceed along the highlighted route.”
Back on I-25, all of a sudden yellow triangles appeared on both navigation displays, with a visual and audible warning of slow traffic ahead; a few seconds later came an indication that an accident had occurred. The nav system immediately zoomed out to show alternate routes with major thoroughfares that would take me around the slowdown. I took the first turn off the Interstate without making any manual adjustments to the system. It routed me effortlessly around the accident and back to I-25. I never pushed a button or had to ask a question. If I’d wanted to continue on secondary roads, it would have accommodated that automatically.
On the outskirts of Denver, I programmed the system to find the nearest Starbucks, which was less than a half-mile off the Interstate. There I reprogrammed my return route to go through seven POIs. Having accomplished this feat without once looking at a manual, I was off again.
I made the trip back on secondary roads mainly so I could cruise with both sun roofs open and listen to the 19 speakers of the wonderful Bose stereo system (Bang and Olufsen option). I stayed about 5 miles below the speed limit and was passed innumerable times, but I didn’t care because I was having so much fun. This automobile is so comfortable, you find yourself looking for ways to extend your journey: 22-way adjustable leather seats; five-way, five-intensity massage system, automatic seat heating/cooling.
I made it to all seven POIs, including a couple I had heard of but never visited before, because of the frustration of getting lost trying to find them. Before I was ready, I found myself back at the dealership. The excellent staff encouraged me to keep the car longer, but frankly I was afraid if I did, it would wind up in my garage, and that is just not in the budget right now. That reminds me, I need to ask for a raise.
Bluetooth connectivity is available; the Apple iPhone can be fully controlled and/or downloaded onto the A8’s terabyte hard drive and accessed from any of the three color touchpad screens in the car.
You can control the GPS navigation interface to include new destinations, from the full color 10-inch touch screens in the rear passenger compartment, giving new meaning to the phrase “back seat driver.” There is a single DVD-CD drive slot in the center dash console as well as a six-disk changer unit in the optionally refrigerated glove box. That is, if the large cooler that extends into the rear cabin from the trunk space is not enough for you. Understandably, the rear cooler is a bit hard to reach from the front seat while you are barreling along the Autobahn at 130 miles per hour, or down I-25 at 75.
Information Everywhere
Bottom line for the Audi A8L: the information you need is displayed almost everywhere you look, and can be called up with the touch of a button, the scroll of a finger, or the sound of your voice. All internal and external data is provided in an atmosphere that is second to none climatologically and ergonomically. It is the only automobile I have driven lately with four full-color touchscreens that, while keeping you situationally aware no matter where you are seated, can simultaneously control all the systems in the automobile. The two 10-inch rear-seat screens can be used to read e-mail, browse the Internet, or watch the latest movies or television programming. Add to this an incredibly performance-minded vehicle, the highest gas mileage rating in its rank, amenities that want to make you slow down and enjoy the journey, and you have my pick for the best GPS-enabled, situationally aware vehicle in its class.
Thanks to Vince Cimino, general manager at the Phil Long Audi dealership in Colorado Springs, and his staff for unfettered access to the Audi A8L and all their expertise.
Until next time, happy navigating.
Burkhard Hunhke, executive director of Volkswagen Group’s Experimental Research Laboratory: “We are now able to keep up with and even surpass the technology in mobile devices.”
Interview with Audi Research Director Burkhard Huhnke
While testing Audis for this article, I had the opportunity to interview Dr. Burkhard Huhnke, executive director of the VW/Audi Experimental Research Laboratory (ERL) in Palo Alto, California. Palo Alto is also home to Stanford University, and thus to Stanley and Shelley, autonomous vehicles that have driven into the record books. ERL supports all brands within the Volkswagen Group: Audi, Bentley, Bugatti, Lamborghini, Seat, Skoda, and Volkswagen.
The integration of external and onboard capabilities with GPS and a screamingly fast new Nvidia Tegra 2 chip make the Audi navigation system the first in-car navigation system with 3-D display capabilities.
Don Jewell (DJ): How is this integrated GPS different from a mobile device adhered to the windshield?
Burkhard Huhnke (BH): Let’s say the driver is overwhelmed in a very difficult situation, like approaching a traffic jam in bad weather at high speed. The Audi will sense this — we call it pre-sense — alert the driver, begin a series of automatic safety measures, such as tightening the seatbelts and closing windows, and then automatically start to brake the automobile. For us, the systems in the Audi are for more than just displaying information or blinking warning lights. The systems actually take over some of the functions and support the driver, especially in emergency situations. GPS provides a way for us to localize the car in its environment with data such as time of day, weather and traffic conditions, and any other information that both onboard and external sensors, such as the Internet and Google, connected provide.
DJ: What happens when GPS data is not available?
BH: We must provide additional sensors and train our systems to learn to bridge the time with GPS outages or interruptions without the driver being aware that GPS is no longer being received, make it seamless. The intelligence, the metadata from other sensors is onboard in the embedded systems, and they are programmed to provide the necessary data when GPS is not available.
DJ: How does this translate to a better experience for the customer?
BH: We put a lot of effort into the optimization of the human-machine interface (HMI). We have psychologists working on the HMI along with our designers and programmers. Some car manufacturers provide systems that force you to think like an engineer to operate them. We realized this approach won’t work. To create an intuitive navigation system requires much, much more. It requires input from our customer, what is intuitive to them. For this as I said we use simulators, customer inputs, along with psychologists, clinical studies, and a great deal of effort that goes into understanding what makes a truly intuitive interface and a system that people will like and enjoy using.
You do not need a handbook to operate our systems. I actually hate handbooks and I believe that if you cannot figure out how to do something, such as program a destination into a GPS in just a few seconds, without a handbook, then the customer will not like it; so we purposely made the system intuitive and very user friendly. The learning curve is very short and our customers find themselves using the system in no time at all.
We found out one of the key things our customers want is beautiful, high-definition, and fast graphics. So we started working with one of the leading companies (Nvidia) for graphical interfaces. In the end, we created an environment in the Audi A8 that is more like your home living room than a normal automobile.
In the A8 we combined the Internet and the onboard Audi network with things like Google Maps so you can continuously download Google Maps as they are needed: beautiful high-definition color graphics and maps with connectivity. The POI search is absolutely as up-to-date as it can be, often including data updated the same day or possibly just a few minutes before from the Internet. In the A8 for a POI you get the same information as if you had searched on your computer at home.
DJ: How much do you care about accuracy for your GPS/PNT systems in the Audi? Is one meter enough?
BH: We are extremely interested in a very accurate GPS position down to the centimeter level. Not all manufacturers are. Since you live in Colorado you may have heard about the Audi TT that successfully drove autonomously up Pikes Peak. To do this, we used differential GPS signals to take hairpin turns at race-like speeds.
But we realized that it is a risk to only depend on external signals such as GPS. GPS information is critical, but we find ourselves depending more and more on our onboard sensors. This gives us a huge advantage, such as with our onboard camera system. It gives us the ability to develop better adaptive cruise-control functions. All these extra sensor inputs combined with GPS gives you the best precision, but when you don’t have GPS, you have to rely on other sensors to take over.
We launched a navigation system with a processor from Nvidia at the same time it was announced as a capability in a mobile device. In the past, we were always behind the time with technology because we were conservative with what we put in the cars, but with this move we are now able to keep up with and even surpass the technology in mobile devices. We created a very smart motherboard so we can exchange and process data quickly.
DJ: What do you see as your mission?
BH: Producing the safest car in the world, and I think we are there. The United States still has 37,000+ traffic fatalities every year, so we took it as our responsibility to create the safest systems onboard any automobile. Our new navigation system predicts curves and safe speeds for the conditions and sometimes automatically reduces the speed of the automobile. We talk a lot about driverless cars, but actually I think we all enjoy driving, like you do, Don, with your Q7 in the snow in Colorado. But there are also times when we are extremely bored and not paying attention to our driving and just wish we could press an autopilot button and start answering e-mails or something. This could be in a traffic jam or any circumstance where it is no longer fun to drive. So that is something we would like to accomplish.
Recently we created a new program with Stanford University to work on solutions for mobility challenges. We want to be able to obtain more external information, use onboard information, and create the car of the future with the smart people at Stanford and those of us at ERL. We want a navigation system that is smart and can predict traffic, which helps and supports the driver, and therefore makes driving extremely safe. That is now our mission.
A new navigation system looks to make driving safer by removing the need for drivers to look away from the road at their navigation device. With Wikitude Drive, as a driver moves down the road, the route is “drawn” onto the live video screen of an Android smartphone.
How is this possible? Augmented reality.
Augmented reality (AR) is a term for a live direct or indirect view of a physical real-world environment whose elements are augmented by virtual computer-generated imagery. The idea to blend augmented reality with navigation struck Philipp Breuss-Schneeweis, founder of Mobilizy, in 2008 when he was developing the Wikitude World Browser for the first Android Developer Challenge. Considering the awards Wikiude Drive has received so far, including being named Global Champion in the 2010 Navteq Challenge, it could be considered the next big advance in consumer navigation.
Wikitude Drive, which launched at the end of 2010, works by attaching a mobile phone on top of a dashboard looking at the road. The application then overlays video captured through the camera with driving instructions. This allows users to drive through their phone, watching the road even while they are looking at directions.
“With Wikitude Drive I don’t find myself looking for directions; the device itself guides me along the way,” said Nicola Radacher, product manager at Mobilizy.
According to Breuss-Schneeweis, Wikitude Drive distinguishes itself from other navigation systems in two ways: First, due to the overlaying of the route onto the live video stream of the surroundings, the driver can easily recognize and follow the suggested route. Instead of looking at an abstract map, the driver is looking at the real world. The navigation system leads the driver through unfamiliar territory in a natural, real, and easy way.
Second, Wikitude Drive solves a key problem that all other navigation systems have. These systems require the driver to take his eyes off the road to look at the abstract navigation map. Just by looking at the map screen for one second when driving at 100 km/h (62 mph), the driver is actually “blind” for 28 meters (92 feet).
“Think about how much can happen in those precious meters. Since Wikitude Drive provides you with driving directions on top of the live video stream, you still see what is happening in front of you when looking at the display of your mobile AR navigation system,” Breuss-Schneeweis said.
The AR system uses data from a smartphone’s GPS, compass, and movement sensors, retrieves information from its database, then displays the information over the camera feed. The company says millions of points of interest will also be displayed when a future version is integrated with Wikitude World Browser, the company’s AR browser for smartphone users.
Wikitude Drive still can be used the traditional way. In some driving conditions — for example when driving in the dark — a drawn map is advantageous, and a driver can switch to the 3D map view by tapping the screen. Voice commands are also provided.
By Chaminda Basnayake, Tom Williams, Paul Alves, and Gérard Lachapelle
Communication-enabled vehicle safety has the potential to change transportation’s future, particularly vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), collectively represented as V2X. An automakers’ consortium conducted extensive field trials to determine GNSS service availability and accuracy for the V2X challenge.
V2X can include applications based on communications between any two or more entities on the road. Of all the potential V2X applications, V2V applications probably lead the way in terms of maturity of prototype development and test efforts. General Motors (GM) demonstrated the first working prototype V2V system in 2005. Information on further industry collaborative efforts in V2V system developments can be found at the U.S. Department of Transportation’s (DOT’s) IntelliDrive website. While a multitude of applications could be developed based on V2I capability, most of the related system prototype development efforts have taken place under the DOT’s Cooperative Intersection Collision Avoidance (CICAS) program.
Driving environments encountered in testing. Clockwise from top left: deep urban, urban thruway, local roads, mountains.
Accuracy Requirements
In terms of positioning accuracy requirements, Vehicle Safety Communications-Applications (VSC-A) prototype system capabilities as well as all V2X applications can be classified as:
Which Road. In this case, accuracy is only required to the extent of identifying the road traveled. For instance, if a vehicle is in a service road parallel to a freeway, knowing that it is on the service road and not on the freeway is sufficient. The need of a typical vehicle navigation device is another good example of this requirement category. The typical accuracy requirement for this case is better than 5 meters. However, this could be a relative accuracy requirement for certain applications. For instance, in a V2V scenario, one vehicle may only need to know if the other is on the same road or not, while in the absolute sense both vehicles could be in error by more than 5 meters. For V2I applications, however, this becomes an absolute accuracy requirement, as the infrastructure is always mapped and identified with respect to a global coordinate frame.
Which Lane. This accuracy level enables applications to identify other entities with lane level resolution. The typical requirement is 1.5 meters or better, which approximately corresponds to half of a lane width. A blind-spot advisor is a good example that requires this accuracy.
Where-in-Lane. This accuracy level enables the relative positioning of entities to better than 1 meter. Further refinements of blind-spot advisor-like applications are examples.
Availability Requirements
GNSS as a line-of-sight technology has obvious limitations in certain environments, and these limitations are well understood by the GNSS community. The focus of this study was to understand the limitations associated with a GNSS-only V2X solution such that requirements for augmentation technologies can be defined. Therefore, no availability requirements were set for the system; estimating availability of a GNSS-only solution was the goal.
Why So Complicated? At first glance, what needs to be done is straightforward; all V2X-capable entities need to be aware of each other’s positions. Hence, if all entities transmit their own location with respect to the same coordinate system, the problem is solved. Unfortunately, it’s not that simple.
Designing the system so that hundreds of entities, potentially using all sorts of GNSS software and hardware, can work together presents a significant challenge. This includes keeping backward compatibility way out into the future.
Even within the same receiver make and type, inclusion of a particular satellite in the solution of one vehicle can significantly affect the solution difference between vehicles. Inclusion of SBAS also contributes as a differentiator. In a V2X scenario, out of two adjacent vehicles, one vehicle may use SBAS while the other may not, due to hardware configuration or visibility. If none of the above situations occurred and everything else were ideal, transmitting just the current horizontal position of a V2X entity over-the-air (OTA) would be sufficient to do everything needed.
V2X thus requires a positioning system architecture that minimizes the impact of these complications and many other potential compatibility issues. Major system design considerations include:
Performance Requirements. The system must provide relative positioning accuracy that fits Which Road, Which Lane, or Where-in-Lane category and should identify the solution quality. For instance, a vehicle on a freeway with relatively open sky view may function in the Which Lane mode and may transition to Which Road mode as it enters an urban area with sky visibility limitations.
Deployment Constraints. The system must be affordable for automotive applications. This may also include considerations such as antenna placement, processing resource requirements, and power requirements.
Bandwidth Constraints. The volume of data transmission constitutes a major consideration for OTA communications. While some methods manage communication range and frequency as a way of optimally using the communication channels, keeping the OTA data volume to a minimum by design was a goal.
Study Goals
This study investigated the performance of two relative positioning methods: DPOS, a method of using the difference in position reported by two entities to calculate the 3D separation between the points; and real-time kinematic (RTK). While there are many other possible relative positioning methods, these two were selected as they collectively represent the most desirable availability and accuracy performance. In DPOS, vehicle coordinates are transmitted between vehicles in order for position differences between vehicles to be derived at each vehicle. In RTK, raw code and carrier-phase data is transmitted between vehicles, and the inter-vehicle position differences are calculated using RTK software in either fixed or float carrier-phase ambiguity mode at each vehicle. The RTK method is more intensive both from a data transmission and computational aspect, but retains only common satellites in the solution, eliminating the problem described earlier. Its use of carrier-phase measurements also makes it more accurate.
The study included two GPS receiver types. The first, a single-frequency L1 automotive-grade receiver, is identified as Type B receiver in this study. The second, identified as Type A, was of a higher quality with proprietary multipath mitigation technologies. Both receivers were capable of using WAAS support. Receiver B also allowed the user to reject selected satellites from its solution. These two devices were selected as they were capable of supporting both processing methods, and represent on the one hand an existing automotive-grade receiver, and on the other hand one that is expected to be a good representation of a product with technologies available for automotive deployment a few years from now.
Specific study goals were:
Accuracy performance of DPOS and RTK methods when all vehicles use same GPS receiver type.
Same when a receiver type or a receiver configuration mix is used.
Dependency of the accuracy performance on the driving environment.
Solution availability with same receiver and mix receiver combinations.
Implications of non-continuous V2I coverage.
Prototype System
The system prototype (Figure 1) used for the study was a replica of the prototype relative positioning system implemented in the VSC-A project. It consists of a dedicated short-range communicatin (DSRC) interface with a DSRC radio, a GPS receiver/relative positioning module, and a sensor data handler.
In operation, a vehicle generates its own location information and GPS raw data in RTCM format and shares this data with other vehicles. OTA messaging was done using the SAE J2735 messages set with GPS raw data in RTCM format attached as optional data. As shown in Figure 1, RTCM v3 1002 messages were used to exchange VSC-A data. The system was also capable of using RTCM v3 messages 1001 & 1005 for V2I operation. The DPOS relative positioning logic was implemented in the sensor data handler, while the RTK implementation was done in a separate relative positioning module. This module takes in local and remote 1002 messages and outputs RTK data to the sensor data handler. Applications could access both RTK and DPOS relative positioning information from the sensor data handler.
Vehicle Setup. Two vehicles were used for the V2X data collection. Four different GPS L1-only test receiver types were installed on each vehicle:
AW: high-quality receiver using WAAS corrections.
BW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections enabled.
BNW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections disabled.
B24W: high-sensitivity automotive-grade receiver using a maximum of the four primary satellites in each of the six planes (minimum guaranteed constellation) and with WAAS ranging and corrections enabled.
As shown in Figure 2, the AW and B type receivers were connected to different GNSS antennas. These antennas were mounted on roof-racks attached to the vehicles (see Photo). The patch antenna for the Type B receivers was mounted on an aluminum-topped wooden pedestal to bring it to approximately the same height as that used by the AW receivers, to provide a ground plane and to prevent shading from other equipment on the roof-racks. The spacing between the antennas was accounted for in all analysis.
Figure 2. High-level V2V hardware setup on each of the two test vehicles.
Figure 2 also shows that only three of the four test receivers, AW, BW, and BNW, were connected to the computer that ran the RTK software. This computer calculated the inter-vehicle vector (IVV) using information exchanged over the DSRC radio link in real time. The vehicles each had a designated base relative to which the IVV was calculated; for Vehicle 1 it was BW and for Vehicle 2 it was AW. Thus the computer on each vehicle calculated three instances of the IVV, for example, the computer on Vehicle 1 calculated BW1–BW2, BW1–BNW2, and BW1–AW2 (where Ri denotes the receiver of type R on vehicle i).
Transmission and reception of data between the two vehicles required for the IVV RTK calculations were achieved using wave radio modules with two magnetically mounted 802.11p antennas on each vehicle for redundancy. During testing, Vehicle 1 generally followed Vehicle 2. To minimize potential interference of roof-mounted instruments on between-vehicle communications, the antennas on Vehicle 1 were located close to the front of the roof, while those on Vehicle 2 were located close to the rear of the roof. In each case, 15 centimeters of roof space were left to provide ground planes for the antennas.
We used the single-point navigation solutions logged from each test receiver to calculate the IVV for each receiver combination using the DPOS method in post-processing. No real-time data transfer between the vehicles was used for this method.
Reference values of the IVV were calculated in post-processing using both geodetic grade GPS/GLONASS L1/L2 receivers and GPS/INS integrated systems in differential mode. Both were connected to the antenna used by the AW receiver. Differential GPS calculations were enabled by using stationary receivers with antennas at precisely known WGS84 locations on top of a building at the University of Calgary.
Two study vehicles with antennas attached to the roof-racks.
Test Scenarios
V2V data was collected in and around the city of Calgary in August 2009. In the majority of the tests, Vehicle 1 followed Vehicle 2 with a separation of less than 300 meters, the stated effective range of the DSRC link. For most tests the inter-vehicle separation was between 30 and 150 meters. Some driving environments forced modifications of the default behavior; for example, on highways, vehicles moved in between the two test vehicles, necessitating lane changes. Approximately 52 hours of data was collected over 12 days. After rejecting data due to various faults such as reference-system malfunction, more than 45 hours of data remained.
Data was collected in the seven test environments listed in Table 1. These environments were selected in accordance with Federal Highway Administration descriptions. Each environment provided different challenges for GNSS-based positioning. Obviously the deep urban environment was challenging because the reduced number of visible satellites and the large amount of multipath meant that navigation solutions were both rare and of poor quality. As another example, the mountain environment was interesting because often almost half the sky was occluded by trees on the mountain side, leading to an asymmetrical visible GPS satellite constellation with the associated solution degradation. The photos at the beginning of this article show selected driving environments encountered during testing.
Table 1. Description of driving environments used in V2V tests.
V2V Solution Accuracy. Positioning accuracy of the individual receiver was first investigated to estimate the V2V relative positioning accuracy when using the DPOS method. This was done for the entire dataset.
Figure 3A shows a representative freeway dataset to illustrate overall trends: the absolute 2D mean position errors observed from all eight GPS receivers used in both vehicles. The first set of four receivers shown were the AW, BW, BNW, B24W receivers in the first vehicle (V1), and the second set of receivers were the same type in the second vehicle (V2). As a general trend, Type A receivers provided better absolute accuracy meeting the Which Lane accuracy, whereas the Type B receivers provided Which Road accuracy. Also, the use of WAAS with receiver Type B has yielded some absolute accuracy improvement. Limiting the constellation to 24 (B24W) did not significantly degrade accuracy in this case.
As a second step, V2V relative accuracy when the same receiver type was used was estimated, and the mean errors are shown in Figure 3B. Based on the mean error for each pair, all four receiver pairs were able to provide Where-in-Lane relative position accuracy. The geodetic grade Type A receiver pair (AW–AW) yields the best relative accuracy at around 0.5 meters relative 2D error. In comparison with the mean absolute errors, the V2V relative accuracy is greatly improved as a result of cancellation of correlated errors, indicating a high degree of correlation of absolute errors in receivers under these test conditions.
The relative accuracy with mixed receiver types or configurations was also estimated. With r
espect to receiver type mixes, the Type A receiver from vehicle 1 was used with the three Type B receivers in vehicle 2, yielding three combinations as AW–BW, AW–BNW, and AW–B24W. Mean error statistics for these three combinations and the combination of BW from vehicle 1 and B24W from the second vehicle are shown in Figure 3C. In comparison to the same type receiver pairing, this shows much larger mean errors. For instance, for all AW receiver mixes, the mean relative error is around 2 meters. Therefore, it is fair to conclude that error characteristics and modeling in the navigation solutions in receiver A and B are type-dependent, and they may not be compatible when a receiver mix is used. The BW–B24W combination does not show a significant increased mean error, indicating that the constellation difference in this test was not significant enough to result in an increased relative positioning error.
Figure 3A. Individual receiver absolute accuracy.Figure 3B. Relative accuracy with same receiver type.Figure 3C. Relative accuracy with receiver/configuration mix.
V2V Solution Availability
Availability statistics were generated for all accuracy categories (Which Road, Which Lane). At a more abstract level, solution availability statistics were also calculated for the DPOS and RTK methods. RTK solutions were defined as available whenever the software yielded a solution for that particular epoch. Data gaps in the RTK method could be caused by either communication failure due to, for example, a large truck entering the line of sight between vehicles, or one vehicle disappearing around a corner, or because insufficient observations from common satellites were available at the two vehicles. DPOS solutions, calculated in post-processing, were defined to be available whenever both receivers had observations from four or more satellites and were therefore able to calculate the necessary independent position solutions. While the two definitions of availability are not quite congruous, because only that for the RTK includes the possibility of communication failure, comparison of logs of data transmitted between the vehicles showed that out of approximately 45 hours of data, only 0.22 percent of missing RTK solutions could be attributed to failure of the DSRC link.
Figure 4 plots the distribution of GPS service outages observed by AW and BW receivers in individual vehicles in all of the test environments including deep urban. Here, as described for the DPOS method, an outage for a single receiver is identified on an epoch basis whenever the receiver has observations from less than four satellites. The total driving time included in this dataset is 45 hours and 4 minutes for each receiver. Figure 4 [deep urban] shows the same statistics for deep urban environment driving only, and this contains 1 hour and 40 minutes of driving for each receiver. The latter was selected specifically as this environment contained the most challenging conditions.
Figure 4. Distribution of GPS service outages for individual vehicles.
An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages lasted less than 30 seconds. However, when using the high-sensitivity enabled Type B receivers, 100 percent of the outages lasted less than 5 seconds. No significant outage difference is seen between the observations from the same receiver type in the two vehicles.
GPS service availability for V2V applications was calculated using two approaches for the two relative positioning methods. For the DPOS method, individual vehicle service availabilities were time-synchronized in post-mission, and V2V DPOS solution availability was estimated. Figure 5 compares V2V solution outages using both receiver types and both relative positioning methods.
Figure 5. Distribution of GPS service outages for V2V applications.
The DPOS method yields better solution availability statistics than RTK. With both receiver types, more than 95 percent of DPOS solution outages are less than 10 seconds. With the RTK method, relatively longer outages were observed, especially for Type B receivers. With Type A receivers, the difference is only significant for outages shorter than 30 seconds. For Type B receivers, larger percentages of longer RTK outages were observed; this can be potentially attributed to poor carrier-phase tracking loop performance of these receivers and the impact on RTK.
Using GNSS Data
We anticipated performance issues arising from receiver type and configuration incompatibilities going into the prototype development effort. We identified use of raw GPS measurements instead of the DPOS method as one method to overcome this limitation, as the differencing techniques with measurement data guarantees correlated error cancellation. This was one reason to include the RTK capability in the prototype system. Therefore, confirming the fact that use of raw measurements eliminates the receiver type and configuration-related incompatibilities was a major goal of the study.
As discussed earlier, V2V relative position solutions using RTK were logged in real time as a part of the test setup. We compared these real-time RTK solutions and the DPOS solutions estimated in post-mission for all datasets. Figure 6 shows three cumulative probability distribution (CDF) plots generated using RTK and DPOS accuracy data from a freeway test dataset. The first CDF plot (left) shows the comparison of accuracy when both vehicles use Type A receivers with RTK and DPOS methods. The second CDF plot (center) shows the same CDFs when both vehicles use the Type B receivers. The third shows the DPOS and RTK accuracy CDFs when vehicle 1 uses Type A receiver and the other uses Type B receiver.
Figure 6 demonstrates that if higher quality GPS receivers similar to Type A are used in both vehicles, both RTK and DPOS methods would provide a solution of better than Which Lane accuracy more than 90 percent of the time. However, if Type B receivers are used, a solution with similar accuracy will only be available 60 percent of the time if the DPOS method is used for relative positioning of the vehicles. If the RTK method is used, this availability can be increased up to 90 percent.
The performance difference between the two methods becomes even more prominent when the two vehicles use a mix of receiver types. In the right-most CDF of Figure 6, a solution with Which Lane accuracy is only available 30 percent of the time if DPOS method is used with the mixed receiver configuration. The RTK solution availability still remains around 90 percent even with the mixed configuration. This confirms that use of measurement data eliminates some of the limitations associated with the DPOS method.
Comparison of only the RTK performance between all three CDFs in Figure 6 shows that RTK V2V performance is only limited by the worst-performing receiver in the receiver combination. Out of the three CDFs, the middle (both vehicles using Type B) and the right (Type A and B mix) CDFs have almost identical RTK performance curves. Given that the RTK curve with both using Type A receivers shows much better performance, it is fair to conclude that in the mixed-receiver case, the RTK curve is limited by the performance of th
e Type B receiver. Figure 6 also shows that at Which Road accuracy, all receiver combinations and both processing methods yield almost identical performance.
Figure 6A. Comparison of V2V solutions using RTK and DPOS methods.Figure 6B. Comparison of V2V solutions using RTK and DPOS methods.Figure 6C. Comparison of V2V solutions using RTK and DPOS methods.
Other Approaches
Given that carrier-phase measurements are subject to cycle slips in some road environments, we ran a test using code measurements only in relative mode, using selected data sets collected on a mountainous highway. Only common satellites were used. Given that code measurements are not affected by a loss of phase lock, such a solution is more robust, but is subject to code noise and multipath. The RMS horizontal position differences between these solutions and the reference inter-vehicle separations were 25 centimeters and 1 meter for receiver Types A and B, respectively. Both receiver types meet the Where-in-Lane requirement in this test. Type A, with its low code noise and excellent code multipath-reduction capability, has a clear advantage.
Such an approach would represent a compromise between the DPOS and RTK approaches. Its advantage over the RTK approach is a lower data transmission-rate requirement, while that over the DPOS approach is the use of common satellites only. The latter is quite significant, since low-elevation satellites contribute the most to horizontal position solutions, but their measurements are affected more by atmospheric transmission errors that are most effectively removed in differential mode on a satellite-by-satellite basis.
V2V Operation with V2I
While infrastructure support can almost always improve the performance of other V2X applications, it can pose a challenge for positioning when such coverage is not continuous. The complication arises as a result of vehicles transitioning in and out of V2I coverage areas. V2I systems are highly likely to include GNSS augmentation capability so that vehicles within a coverage area benefit from better positioning capability. However, when vehicles transition from standard (V2V) operation mode to a V2I enhanced mode, some effects in the vehicle position domain can pose potential challenges for DPOS-based V2V.
The field study included test scenarios with limited V2I coverage in different driving environments: all of those described above with the exceptions of deep urban and mountains. In deployment, the infrastructure points (IPs) would broadcast aiding information to the vehicles within their coverage area, allowing real-time calculations. In the field study, in which the role of the IP was filled by a stationary high-grade receiver with a tripod-mounted antenna, all V2I estimates of the IVV were calculated using post-processing. Further, V2I estimates of the IVV were only calculated when at least one of the vehicles was within the coverage area of the IP, here chosen to be a circle of radius 300 meters centered at the IP. This range was chosen since it is the nominal effective range of the DSRC link.
The location of the IP, that is, the phase center of the stationary antenna, was determined using commercial RTK network software with additional stations at precise locations on the rooftop of a building at the University of Calgary. The estimated accuracy of this position was 5 millimeters (1 sigma). The distances of the vehicles from the IP, used to indicate when the vehicles transitioned into and out of the IP coverage area, were determined using the GPS/INS reference trajectories. In post-processing, once a vehicle was identified as having entered the IP coverage area, commercial RTK software was used to estimate the position of the vehicle, using the IP as base and each of the test receivers on that vehicle as rovers. The IVV was then calculated using the difference of the positions of the two vehicles. Thus, the V2I estimate of the IVV was determined using what is essentially the DPOS method with stationary base RTK-indicated vehicular positions, instead of the less accurate single-point GPS position solutions. When only one vehicle was within the coverage area, single-point solutions were used for the distal vehicle, resulting in a solution called V2I-S.
Figure 7 shows two sets of CDFs generated to illustrate the V2V positioning accuracy with V2I capability. The left plot corresponds to AW–AW receiver combination, and the right plot corresponds to the BW–BW combination. Each plot includes four curves. One pair of curves shows the V2V positioning accuracy without V2I, which includes performance when using the DPOS method (green) and another when using RTK (blue). The second pair shows the accuracy of the V2I and V2I-S estimates.
The most striking observation from Figure 7 is the separation of the V2I-S case from others for both receiver combinations (purple). It shows much worse positioning accuracy compared to the other three curves. For instance, using a BW–BW pair, the system will meet the Which Lane accuracy requirement around 80 percent of the time for either DPOS or RTK V2V without V2I support. However, when V2I coverage is available to only one vehicle, the V2I-S case, the accuracy requirement is only met at 40 percent confidence.
Figure 7A. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).Figure 7B. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).
Thus, system accuracy performance degrades when vehicles are operating in DPOS mode and are transitioning in and out of the V2I zones. This is because the V2I-S estimate is the difference of an accurate position solution for the vehicle within the coverage zone, and a potentially inaccurate single-point solution for the one outside the coverage zone. The beneficial cancellation of similar errors that occurs for DPOS estimates (using similar receivers and with common satellite observations) does not occur for V2I-S.
Potential solutions to this problem include using a V2I method of IVV calculation that is not dependent on the estimated position alone (that is, use RTK or other measurement-based methods as opposed to DPOS), or using a position-mode indicator with the DPOS mode such that a DPOS-based V2V solution is only generated when both vehicles are operating in the same mode (that is, V2I). However, the latter does not provide a remedy for the complications when the two vehicles are operating in two different modes. One could also consider a variation of the latter method whereby a V2I-augmented position and a non-augmented position is maintained by each vehicle, such that one of them could be used to generated a mode-matched DPOS V2V solution for a given sender.
Recommendations
These extensive trials provided valuable data demonstrating technical challenges associated with V2X positioning.
Error characteristics and modeling in the navigation solutions in receivers A and B are type-dependent, and they may not be compatible when a receiver mix is used with the DPOS mode. This is very likely to be the case for many other commercial receivers. Therefore, it is important to develop receiver hardware and software minimum-performance standards that define acceptable performance for measurement quality, satellite tracking and selection criteria, reliability estimates, navigation-solution parameters, and other such indicators.
Findings with RTK confirm the fact that use of measurement data eliminates some of the limitations associated with the DPOS method. While RTK is the most demanding raw data-based method in terms of processin
g requirements and OTA data needs, the study also conducted limited investigation on other methods that use raw code data and are less resource-intensive, and at the same time better performing than DPOS. Such an approach would represent a compromise between the DPOS and RTK approaches.
An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages were less than 30 seconds. These statistics are useful for future research on suitable GNSS augmentation methods.
System accuracy performance degrades when vehicles operate in DPOS mode and transition in and out of the V2I zones. Potential solutions should be incorporated into the systems to take care of these limitations.
Acknowledgments
The authors thank the Crash Avoidance Metrics Partnership Vehicle Safety Communications-Applications team, in particular the Vehicle Positioning Technology Development team, for input. This work was conducted as a part of a CAMP VSC-A project under a cooperative agreement with the U.S. DOT.
CHAMINDA BASNYAKE is a senior research engineer at General Motors Global Research and Development and GNSS technology expert for GM OnStar. He leads GNSS-based vehicle navigation technology R&D efforts at GM and holds a Ph.D. in geomatics engineering from the University of Calgary.
TOM WILLIAMS is a postdoctoral researcher in the PLAN group in the Department of Geomatics Engineering at the University of Calgary.
PAUL ALVES is a Calgary-based geomatics consultant specializing in RTK. He obtained his doctorate from the University of Calgary.
GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.
A virtual reference station network covering a metropolitan area supplies position corrections to commuter buses equipped with a driver-assist system to enable safe operation, even under harsh weather conditions, along high-volume roadways.
By Craig Shankwitz
Bus-only shoulders on major traffic arteries enable a bus to travel on typically unused road right-of-way, bypassing congestion during peak rush hours. As the shoulder is typically only centimeters wider than the bus itself, lane-keeping becomes a key factor, and is accomplished in a pilot Minnesota project using dual-frequency, carrier-phase differential GPS (DGPS) as its primary positioning technology. DGPS provides position estimates accurate to 5–8 centimeters at a rate of 10 Hz, and is used to determine vehicle position and heading. An on-board map database is used to determine the position, orientation, and trajectory of the vehicle relative to the roadway.
Use of the shoulder as a busway offers several construction and operational advantages:
Ease of Implementation. The shoulder exists; there is no need to acquire and develop additional right of way.
Low Costs. The cost to strengthen and modify an existing road shoulder is significantly less than constructing a new busway.
Routing. Because bus-only shoulders follow existing routes, no changes to bus routes, bus stops, or transit stations are needed to support bus-only shoulder operations.
Customer Satisfaction. Transit customers who travel on buses that use a bus-only shoulder perceive a travel-time saving two to three times greater than actually realized. Keeping the bus moving at all times offers a significant psychological advantage.
Increased Ridership. A 1997 study of bus-only shoulders in the Twin Cities analyzed more than nine bus-only shoulder routes for two years and found a 9.2-percent increase in ridership along these routes. At the same time, total ridership had decreased by 6.5 percent.
However, the use of bus-only shoulders imposes additional stress and strain on a driver. The narrow bus-only shoulder leaves a driver very little margin of error. Operating within this small margin is difficult even during the best traffic and weather conditions, and degrades to nearly impossible during heavy traffic and poor weather conditions, which are frequent during Minnesota’s notoriously hard winters.
During difficult weather and traffic conditions, the use of the bus-only shoulder offers its greatest transit advantage. If a driver is unable to utilize the bus-only shoulder, this advantage is lost. A properly designed and executed driver-assist system (DAS) enables a driver to use the shoulder under all conditions, thereby increasing schedule adherence and, as a result, rider satisfaction.
Under the U.S. Department of Transportation’s Urban Partnership Agreement, the University of Minnesota’s Intelligent Vehicles Lab (IV Lab) and HumanFIRST program, the Minnesota Valley Transit Authority (MVTA), and Schmitty and Sons Transportation will soon deploy DAS on 10 Gillig low-floor transit buses. These buses will provide express service between Apple Valley and downtown Minneapolis, a 22-mile, one-way trip.
Driver-Assist History
The IV Lab has developed and deployed DGPS-based DAS since 1995. The first deployment on public roads occurred in 2001, as part of the DOT’s Intelligent Vehicle Initiative Generation Zero Field Operational Test. The DGPS-based lane-keeping assistance was integrated with forward-looking radar for collision avoidance, enabling safe vehicle operation in zero-visibility conditions.
Two separate deployments took place in Alaska. The first occurred in 2003 with a snowplow and a snowblower which clear the Thompson Pass on the Richardson Highway. These vehicles are still in use. Because of this success, the State of Alaska installed the DAS in two more vehicles at Deadhorse Airport.
During the summer of 2010, the two original Thompson Pass systems will be upgraded with new computational hardware, and three new systems will be installed on three new highway maintenance vehicles. The value of the driver-assist system has been proven, and those who use it have grown to rely on its all-weather capabilities. It has functioned reliably for seven years in extremely harsh conditions.
The DAS provides two primary capabilities for transit applications: lane-keeping and collision awareness. The system provides assistance only; a driver is always responsible for control of the vehicle. Figure 1 shows the components comprising the DAS.
Figure 1. Complete driver assist system component schematic, showing both infrastructure-based and vehicle-based components.
DGPS-Based Lane-Keeping. The primary positioning sensor used aboard the buses is a dual-frequency, carrier-phase GNSS receiver, providing centimeter-accurate position measurements at 10 Hz. With the exception of the DGPS augmentation system described later, all other DAS system processes are synchronized with the arrival of DGPS position updates.
Realtime CMR+ DGPS corrections are provided over the 3G cellular network from the IV Lab VRS network. The IV Lab VRS network is based on six receivers located around the perimeter of the Twin Cities Metro area. These six receivers are connected via landlines to a server system located in the IV Lab at the University of Minnesota, running GPSnet and RTKnet applications. To ensure GPS correction reliability, an integrity manager software issues alerts for both short-term and long-term aberrations in the data provided by the six base stations. This ensures accurate corrections are sent to the buses using the narrow shoulders.
The onboard receiver also plays a crucial role in accurately estimating vehicle body heading. In rural applications where GPS augmentation is unnecessary, GPS velocity heading estimates provided directly from a GPS receiver serve as a sufficiently accurate body-heading estimate. However, in GPS-denied environments where an augmentation system is needed to provide accurate position and heading estimates when GPS is lost, velocity heading from an onboard receiver is an insufficiently accurate estimate of vehicle heading. To support such navigation, the IV Lab developed a technique, described later, by which body heading can be estimated with errors less than 0.1 degree.
IV Lab mapping rig installed in a pickup truck: three dual-frequency, carrier-phase DGPS receivers; two laser scanners, one measuring retroreflectivity, the other road crown and rutting; and forward and sideview cameras, to help analyze anomalous data.
Map Databases
Lane-keeping uses DGPS with an onboard map database describing the location and type of lane boundaries and other relevant roadway elements to an accuracy of approximately 10 centimeters. These map databases can be constructed in one of three ways:
from sufficiently accurate photogrammetric data,
by driving centerlines and using known road-construction standards to d
etermine the location of lane boundaries and other relevant elements relative to the lane centerline, or
by using a combination of laser scanners, DGPS receivers, and cameras to determine the global location of the reflective markings that bound lanes and shoulders.
Lane-keeping information is continuously provided to the driver; lane-departure alerts and warnings use a comparison of vehicle speed and heading to the map database to determine when alerts and warnings should be issued.
The alerts and warnings are provided via a multi-modal human-machine interface (HMI), illustrated in Figure 2, through three modes:
graphically, through a head-up display (HUD) that gives a virtual view out the windshield when environmental conditions limit visibility;
haptically, through a torque-actuated steering wheel giving a restorative torque on the steering wheel in the event of lane drift; and
tactically, through a seat equipped with actuators that vibrate on the side of the seat to which the lane is being departed.
Figure 2. Multi-modal driver interfaces. Left: Graphical, haptic, and tactile feedback modes provided to the driver. Upper right: View through the head-up display. Graphical lane departure alert indicated by left shoulder boundary colored red, collision awareness alert (white rectangles), and collision awareness warning (red rectangle). Lower right: Forward, left, and right side collision awareness information presented on the display on the left “A” pillar.
Lane-departure warnings come in stages. As the vehicle-trajectory estimator determines that the likelihood of a lane departure is sufficiently high, a lane-departure warning is issued to the driver through the HUD: a change in lane boundary color from white or yellow to red. Should the driver contact the lane boundary, a seat-based warning is activated; the side of the seat corresponding to the direction of lane departure vibrates, warning the driver. If the driver fails to respond to these two stimuli and continues past the lane boundary, the steering motor torque is applied. This multi-stage approach captures the drivers’ attention, but if they respond in a timely fashion, their annoyance is limited.
The torque applied by the steering servo motor is limited, and cannot deliver sufficient control action to autonomously steer the vehicle. This is by design; the driver is responsible for operating the bus. The level of torque applied to the steering wheel is analogous to an automotive front-end misalignment; it is sufficient to capture the drivers’ attention, but not to steer a bus off the road.
Forward-Collision Awareness. Sensing for forward-collision assistance is provided by a front bumper-mounted multi-plane scanning LIDAR sensor. Forward-collision alert and warning information is provided in two stages to the driver through the HUD. As now configured, if the obstacle detected is in the present shoulder of travel, the obstacle is represented as a red, open rectangle, with red indicating a warning status. If an object is located in an adjacent lane, the obstacle is represented as a white, open rectangle, with white indicating an alert status.
Obstacle-detection processing is enhanced by the presence of the onboard map database used for lane-keeping. Obstacle target information provided by the LIDAR sensor includes range, range rate, and azimuth angle to the target. The bus position and heading is provided by either DGPS or the DGPS augmentation system. Through a coordinate transformation, LIDAR information in the vehicle coordinate frame is transferred to the global coordinate frame. This allows the LIDAR target to be placed on the map database; if the target is in the vehicle lane of travel, it can be considered a threat, but if the LIDAR target is not in the same lane as the bus, then at that time the target is not a threat to the driver.
Side-Collision Awareness. Side collision awareness is enhanced by multi-plane LIDAR scanners mounted on on the front bumpers on both the left and right sides of the bus, and connected to a pneumatic actuator.
Side-collision awareness information is provided to the driver via an LCD panel mounted on the left front A-Pillar (see Figure 2). This display is touch-sensitive, and can be used by the driver to log in (only certified, trained drivers can operate the system) to select feedback modalities (choose any or all of the available feedback modes) and to check system status.
SIDE-MOUNTED LASER SCANNER used for both side-collision awareness and DGPS augmentation. When extended, the LIDAR scans 100 degrees of the horizontal plane. One boundary of the scanned plane points behind and runs alongside the bus; the other boundary points forward of the bus by approximately 10 degrees.SIDE-MOUNTED LASER SCANNER used for both side-collision awareness and DGPS augmentation. When retracted (right), the LIDAR points in the direction of the ground, and can be used for curb-following when DGPS is unavailable.
Suburban and Urban
Although the rural implementation of the DAS operates in extremely harsh weather conditions, these implementations are technically less problematic than suburban and urban implementations. In rural applications such as the snowplows, DAS-equipped vehicles typically operate with a single occupant in a small geographic area, travel on relatively low traffic-volume roads, and enjoy a clear view of the sky. Suburban and urban applications carry passengers, operate across a wider geographic area, travel on high-volume roads, and suffer from periods where view of GPS satellites is either partially or completely blocked.
These operational differences require substantial changes to the DAS subsystems for urban/suburban use.
DGPS Base Stations. In rural areas, DAS-equipped vehicles typically operate over a relatively small geographic area; a single GPS base station will provide adequate coverage as the maximum baseline between rover and the base station remains less than 25 miles. Suburban applications cover a much wider area, and a network of DGPS correction stations is needed to keep baselines low.
For the UPA project, the IV Lab operates a six-station virtual reference station (VRS) network. This network covers the greater Twin Cities Metropolitan area, and supplies compact measurement record (CMR) corrections to each DAS-equipped bus. Satellite observables are sent from each base station receiver to both the VRS server at the IV Lab and to a VRS server at the Minnesota Department of Transportation.
Broadcast of DGPS Corrections. In rural areas, the DAS system has served to keep roads passable in inclement weather conditions. This has been viewed as a safety application, and as such either UHF or VHF channels in the public safety bands have been used to broadcast DGPS corrections. In urban areas, no single UHF or VHF frequency is available to cover an entire metropolitan area. Therefore 3G cellular data communications are used to provide DGPS corrections to DAS-equipped vehicles.
Use of 3G cellular data communications brings the transit customer an added benefit: free Wi-Fi. The provision of DGPS corrections, using the CMR+ correction format, requires approximately 10 Kbit/second. This bandwidth is assigned high priority by the onboard router. The remaining 700 Kbit/s of 3G bandwidth is made available, at a lower priority, to bus passengers. On an express route service, passengers can e-mail and surf the web on their daily commute, making productive use of
time that might otherwise be lost.
The VRS server provides a unique correction to each DAS-equipped bus. Communication between the bus and the VRS server is initiated by the bus when it sends its coarse (uncorrected) position to the server. The server replies with a correction optimized for that coarse location. Corrections are sent at one-second intervals. Every two minutes, the bus sends its current position, and the VRS server responds with corrections optimized for that new location. With this scheme, the baseline between the VRS and the roving bus is never more than two miles. The two-mile limit maintains position accuracy without consuming excessive wireless or computational bandwidth.
DGPS Redundancy. In rural applications, the view of the sky is generally unobstructed, and FCC licenses provide adequate effective radiated power from the DGPS base stations. This assurance of access to both satellite and corrections signals generally suffices to support uninterrupted vehicle positioning. Both base-station and onboard GPS hardware have proven to be robust and reliable. With these local operating conditions, public agencies have found no need to augment DGPS for rural applications.
Suburban and urban applications, however, require an augmentation system to support DAS operation when DGPS is unavailable due to outages caused by overpasses, overhead road signs, tree canopies, and so on. Passenger safety and the need to provide reliable schedule adherence require that positioning be provided even when DGPS is unavailable, by a vehicle-based DGPS augmentation system.
Vehicle-Based Augmentation
The vehicle-based augmentation system (VBAS) uses direct measurements of ground velocity, a measure of vehicle yaw rate, and an accurate estimate of the vehicle position and heading at the time DGPS is lost to estimate vehicle position and heading for the duration of signal loss.
A commercial off-the-shelf sensor designed for measuring vehicle and/or tire slip measures vehicle 2D velocity. Yaw rate can be measured either with an inertial rotational rate sensor or a second 2D velocity sensor. Yaw rate measured using a pair of these 2D sensors eliminates the rate bias and rate bias drift associated with inertial sensors. Figure 3 shows both configurations.
FIGURE 3 Two approaches to VBAS to mitigate DGPS outages. The diagram on left shows implementation with two 2D velocity sensors to determine vehicle yaw rate. Computationally, this is attractive as senor drift need not be considered. The diagram on the right shows an implementation with one yaw rate sensor, and one 2D velocity sensor. This is the configuration operating for the UPA; it requires yaw rate sensor drift compensation to provide accurate measures of vehicle yaw rate.
An accurate measure of vehicle heading at the time GPS positioning is lost is critical to the augmentation process. A performance goal of 20 centimeters tolerable error at the end of a 15-second outage for a vehicle traveling at 25 miles per hour (11.2 meters/second) requires a heading estimation error of no more than 0.07 degrees (that assumes the only source of error is attributable to the heading).
GPS outages (time from loss of position to reacquisition) attributed to passing under overpasses range from 7 seconds (single bridge) to 9 seconds (double bridge). The IV Lab augmentation system reliably provides sufficiently accurate position and heading estimates to carry through these outages. At the present level of performance, should an outage last more than 15 seconds, the accuracy of the augmentation system cannot be guaranteed. In this event, the driver is alerted, and the DAS is deactivated until a DGPS position fix is reacquired. Fortunately, since new receiver firmware was installed, no instances of an outage exceeding 15 seconds have occurred during two months of test, evaluation, and driver training.
Figure 4 illustrates the accuracy of the VBAS system. At the time the fix solution is reacquired on the exit ramp, the lateral error between the fix solution and the position estimated by the VBAS is approximately 10 centimeters. This accuracy is sufficient to allow a driver to travel on the entrance ramp even during zero-visibility conditions.
Figure 4. Example of VBAS as a bus operates on the Cedar Avenue/Old Shakopee Road overpass. Bus trajectory is northbound on Cedar, exiting westbound Old Shakopee Road, then entering southbound Cedar Avenue from Old Shakopee Road. Upper left shows northbound trajectory and loss of satellite lock. Upper right shows reacquisition of DGPS, float, and fix states of the DGPS receiver. Lower right shows accuracy of VBAS system compared to DGPS when DGPS reacquires fix. Lateral error of VBAS at at the time the fix is reacquired is approximately 10 centimeters. Lower left shows satellite view of the interchange.
Driver Training
Bus-only shoulder operation has proven itself safe and, in fact, safer than normal transit operations, according to recent data. The goal of driver training is to prepare drivers to use the DAS system to enable them to safely use the bus-only shoulders in conditions under which they normally would not.
A rigorous training protocol developed in cooperation with the University of Minnesota HumanFIRST program, Schmitty and Sons Transportation driving instructors, and MVTA involves both simulator-based and on-road training.
Simulator-Based Training
Beefore using driver assist systems, bus drivers are continually taught that the driver controls the bus and is responsible for both the passengers and vehicle. Drivers take this responsibility seriously, and as such, develop skills and techniques that guarantee safe passage under all conditions, even when running on narrow, bus-only shoulders.
To best prepare drivers for using the DAS under difficult conditions, a high-fidelity driving simulator was commissioned. A DAS was installed in the simulator, and an interface to the simulator was created. In this context, a driver has the ability to train in normal and abnormal (low to zero visibility) conditions before beginning on-road DAS training and use.
In the simulator, the driver learns that the system only provides assistance; responsibility for the safety of the bus and passengers still resides with the driver. Experience with Alaskan snowplow operations, where formal training is limited to a few on-road test drives, has shown that a driver may take a few winter seasons to fully accept the system. This delayed acceptance is in part attributable to the fact that for six months per year a driver has no opportunity to train with the system. Acceptance gained over one winter season is lost during the summer.
The simulator installed at an MVTA bus garage uses a seat-based motion platform to achieve realistic vehicle dynamics. The DAS installed in the simulator allows a driver to train in all weather and traffic conditions on a geospecific roadway before transitioning to a DAS-equipped bus. Geospecificity is achieved through the creation of virtual worlds based on roadway data collected by the mapping vehicle shown earlier.
Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.
On-Road Training
After a driver both demonstrates an
d acknowledges comfort and competence with the DAS in the simulator, training transitions to the actual route on which the buses will operate. Each of the 10 buses is equipped with a six-camera data-acquisition system. The six cameras capture not only the driver’s actions (hands, face, feet), but also views of the road (front, left, and right sides.)
Drivers travel with an instructor. The onboard data acquisition system can be used to reconstruct particular scenarios as a means to offer advice as to how the driver and system can better interact in difficult driving and traffic conditions.
On-road training benefits system developers as well. Training offers a driver an opportunity to test the system in real-time on an actual road. The perspective a driver brings is generally different than that of the developer, and the insights the end user provides typically produce a better system. As an example, driver experience with the system during the initial training period produced the staged approach to lane-departure alerts previously described.
Conclusion
The IV Lab, MVTA, and Schmitty and Sons Transportation will soon release 10 DAS-equipped buses into revenue service to support narrow bus-only shoulder service between downtown Minneapolis and Apple Valley, Minnesota. Although the IV Lab has deployed a number of DAS-equipped vehicles, this UPA deployment represents the first time that the system has been used to transport passengers. This deployment should prove that although DGPS systems are susceptible to periodic outages, a properly designed and executed augmentation system will provide a sufficiently robust system that will be accepted by both drivers and passengers. It will also demonstrate to other transit agencies that even narrow rights of way offer significant transit advantages at low cost, and that potential operational difficulties can be overcome through the use of DAS technologies.
Manufacturers
The buses carry Trimble R7 receivers and Ibeo Lux multi-plane scanning LIDAR sensors. The IV Lab VRS network is based on six Trimble NetR5 receivers. The server runs Trimble’s GPSnet and RTKnet applications, with the Trimble Integrity Manager.
Craig Shankwitz is the director of the Intelligent Vehicles Laboratory at the University of Minnesota.
A team will attempt to shatter the world land speed record, with a GPS/GLONASS receiver riding the controls.
photo: GPS/GLONASS
In summer 2010, a team of 44 volunteers will attempt to shatter the world land speed record of 763 miles per hour (2 mph faster than sound) by hitting 800 on the speedometer. To ensure that the North American Eagle takes it to the limit efficiently and safely, the team captures performance data from 70 sensors, locked to position, velocity, and time coordinates by an onboard GNSS receiver.
An old Lockheed F-104, a ’60s era Mach II fighter, rescued from a scrap dealer in Maine seemed to have the mark of greatness somehow still on it, amid the fuselage holes and grafitti. A team of volunteers converted the plane into a supersonic car: one expert machined the solid billet aluminum wheels, another rebuilt the General Electric J-79 engine, another devised a magnetic braking system. Over three years, the team replaced 40 percent of the plane’s skin panels, 5,000 rivets, the front suspension, and the steering and hydraulics systems.
The 56-feet-long, 13,000-pound car features Magna Force Lev-X magnetic brakes, a stock engine that outputs 42,500 horsepower, burning 160 gallons of fuel per minute in afterburner mode, and a backup 52,000-hp engine.
At supersonic speeds, anticipating and controlling the car’s reactions to physical stresses become critical. explains Steve Wallace, data-acquisition engineer: “Stuff happens when you get up to the speed of sound. Shock waves affect the aerodynamic balance of the vehicle, and when you’re flying six inches from the ground, aerodynamic unbalance becomes very important.” Wallace and the team must ensure that the car does not burrow into the ground or lift any of its wheels while in motion; either would be detrimental to record-setting and driver safety.
Accelerometers, strain gauges, piezoelectric sensors, inertial gyros, airspeed and air-pressure sensors, and more all generate streams of data, stored on a laptop computer mounted behind the car’s cockpit. Wallace accesses the system in real time via an Ethernet wireless umbrella of routers mounted on 20-foot-tall towers spaced at 2½-mile intervals alongside a 14-mile track at Black Rock Desert, Nevada. After a test run, he downloads the data to his computer, correlates time and location, then exports to a spreadsheet.
GNSS makes speed measurement more reliable and accurate. “We have a lot of sensors, but one sensor I don’t have is for vehicle speed,” he says. “This is a thrust vehicle; the wheels aren’t driven, so realistically, the wheels are never going as fast as the ground. Measuring airspeed is not a bad way of [measuring vehicle speed], but it’s very noisy. The data are all over the place and you have to do a lot of smoothing and averaging. With GNSS, it’s dead nuts — a great way of getting information I need, fast, without looking at accelerometer data. I can’t think of a more valuable tool to understand what’s happening from a sense of motion of the vehicle in general.
“If we had a $10 million budget, we could buy a ground-tracking radar unit like the Air Force uses, but that ain’t gonna happen. We can get just as good data with this as with a $10 million system, so why go any further?”
The project could provide further practical engineering benefits. The team likens this research to that of the 1960s space program, which benefited development of computers, cellular phones, and microwave ovens.
Manufacturers
A dual-frequency Topcon PG-A1 antenna and Euro 160T OEM receiver collect GPS/GLONASS signals at 20 Hz. A Euro 160T mobile control board rides in the car’s electronics bay. A GB-1000 receiver collects static reference data.
CSR plc of Cambridge, UK, and SiRF Technology Holdings Inc., of San Jose, California, on June 26 completed the merger between SiRF and a wholly owned subsidiary of CSR. The merger resulted in “creating a provider of connectivity and location platforms and a company with the scale, technology, and strategy to enable its customers to address the exciting and emerging opportunities in mobile markets,” according to a company statement.
The company said that customers of the enlarged CSR group will be able to deliver new user experiences of connectivity and location technologies in a diverse range of devices such as mobile phones, personal navigation devices, in-car navigation and telematics systems, laptop and netbook PCs, mobile internet devices, digital cameras, gaming machines, cellular accessories, and consumer electronic devices.
“In bringing together the combined capabilities and broad range of CSR and SiRF technologies and platforms, we have created a new force in the industry and a world-class organization with the commercial, technical and operational scale to build on CSR and SiRF’s existing customer relationships and deliver the next generation of connectivity and location enabled products,” said Joep van Beurden, CEO of CSR. “Our strategic goal is to address the existing and emerging needs of our combined customer base for connectivity and location technologies. The potential applications and benefits to the end user of connectivity plus location are only just starting to open up, and these exciting new opportunities will be driven by our unique combination of leading location technologies and connectivity solutions.”
“CSR and SiRF have a shared vision of using innovation to bring the benefits of wireless connectivity and location to mainstream consumers and enterprises and to enable new and exciting user experiences”, said Kanwar Chadha, co-founder of SiRF and newly appointed board member and chief marketing officer of CSR. “We believe that through this merger, our customers and consumers will derive benefits from a much stronger player whose focus is on delivering best in class connectivity and location platforms.”
“Technology innovation represents the foundation for both CSR’s and SiRF’s success in the market place,” said James Collier, co-founder, board member and Chief Technology Officer of CSR. “We look forward to combining the complementary expertise of our teams to take innovation to the next level in our multifunction radio and system platforms to address emerging customer and market needs.”
For CSR’s customers, the merger with SiRF means CSR’s Connectivity Centre products are augmented by GPS technologies that are well respected and enjoy widespread adoption, the company said, while SiRF brings to CSR a strong IP portfolio in GPS and assisted GPS (A-GPS), dead reckoning, and location centric platforms. The enlarged CSR group will have its global headquarters in Cambridge, UK, with SiRF’s headquarters in San Jose becoming CSR’s U.S. headquarters.
This article addresses how best to quantify “which navigation system performs best” in a realistic testing scenario. The methodology focuses on land vehicles navigating in urban environments, but applies equally well to pedestrian navigation and can be adapted for testing assisted-GNSS implementations. During a drive test, the truth-reference system and RF recording system log samples to disk, with no need for the receivers under test to be included during the actual drive.
By Eric Vinande, Brian Weinstein, Tianxing Chu, and Dennis Akos, University of Colorado, Boulder
FIGURE 1. Traditional in-vehicle receiver testing.
Radio frequency record-and-playback systems (RPS) have recently become commercially available. These systems sample the RF environment and store it to disk during a drive test and can replay it through receivers back in the lab environment. Here we explore the improvements in dynamic testing methodology created by these units.
RPS test system installation.
RPS constitute a stark contrast to more traditional signal simulators that use pre-defined trajectories and mathematical models to determine appropriate RF output. Signal simulators attempt to reproduce environmental error factors such as multipath, inertial aiding system errors, and building and vehicle obstructions. They rely on mathematical models to simulate these various error sources. In some cases they do a reasonable job of reproducing these errors, but the dynamic urban environment is so complex (for example, rapidly varying/fading signal strength(s), multiple multipath signals, short/long duration obstructions of multiple layers) that even a sophisticated mathematical model can not replicate all effects completely. Some simulators include software that enables the user to define a trajectory and a limited amount of urban scenario details. Again, only so much realism can be created in a simulation environment. Existing testing standards are simulator-based, and as such, are circumscribed by the signal simulator limitations in representing a dynamic environment.
Positioning performance of a satellite navigation receiver under test (RUT) is coupled with its RF front-end system and local oscillator quality. Because of the variation in RF components between RUTs, some likely have superior RF interference (RFI) immunity. RFI can be a serious issue in certain land vehicles due to on-board electrical systems or because of external interference sources.
This article describes a testing method applicable to all receiver types, and complementary to that described in the December 2009 GPS World article by Mitelman and colleagues, “Testing Software Receivers,” regarding validation testing within a production environment. Added elements include taking into account truth-system uncertainty and a repeatability verification of the RF playback process through non-deterministic hardware receivers.
We present here the dynamic testing approach currently used at the University of Colorado in Boulder for receiver evaluation and comparison in the urban environment. The approach also includes the ability to assess the effect of sensor augmentations (for example, inertial, environmental) on positioning performance.
Truth Reference. Comparison with a truth reference system is essential for evaluation of satellite navigation receivers. For dynamic testing, this typically includes a survey-grade receiver coupled with a tactical-grade (or better) inertial measurement unit (IMU) and associated carrier-phase differential post-processing software. This software is filter-based and provides a positioning-error estimate in various components. Truth reference systems provide a continuous position estimate whose quality can vary depending on factors experienced in the urban environment, including length of full/partial satellite signal outage. In this study, we subtracted the 99th-percentile horizontal positioning error estimate of the truth system from the nominal RUT positioning error at each reporting epoch, as shown in Figure 2.
If the RUT position happens to lie within the truth-system position uncertainty, it is not considered to have any position error.
We focus here on a method to evaluate and compare mass-market, consumer-grade receivers to survey-grade receivers. One difference between these two receiver types is the way they handle the trade-off between accuracy and availability. Consumer receivers strive to provide the user with the highest availability, whereas survey receivers’ goal is to maximize accuracy. As a result, consumer-grade receivers will produce more regular position updates in harsh signal-tracking conditions, but must sacrifice accuracy to do so.
FIGURE 2. RUT position error calculation
Current Testing Standards
Currently accepted A-GPS standards such as those used by the 3rd Generation Partnership Project (3GPP) provide very limited dynamic testing in simulated urban conditions, being mainly designed to evaluate the first position calculation achieved in a particular simulated scenario. High-sensitivity receivers that pass or greatly exceed the 3GPP tests, in our opinion, are not guaranteed to have superior navigation performance in urban areas. Also, local oscillator performance is not specified. The trajectory dynamics imposed can actually be much smaller than the clock dynamics of a very low-cost local oscillator. A GPS receiver cannot tell the difference between the two and must track the effective Doppler variation.
The 3GPP defines five independent tests for A-GPS receiver certification. They include tests in the areas of: sensitivity with coarse/fine time assistance, nominal accuracy, dynamic range, multipath performance, and moving scenario/periodic update performance. The last three tests include elements that ostensibly pertain to the urban environment. These tests specify discrete, constant signal power levels for implementation in a hardware signal simulator. The discrepancy between the 3GPP-prescribed signal levels and those observed during actual drive testing is detailed as follows.
The 3GPP moving scenario/periodic update performance test trajectory is shown in Figure 3.
This test profile calls for the simulation of five satellites with a constant signal strength of 2130 dBm while the vehicle travels around the racetrack trajectory. In contrast, during an actual drive test in an urban area, a receiver reported the distribution of carrier-to-noise-density values for all tracked satellites as shown in Figure 4. This more accurately shows the range of signal strengths that should be expected in urban conditions.
FIGURE 4. Drive-test C/N0 distribution
The 3GPP moving test is considered passed if positions are reported regularly, and 95 percent of them are within 100 meters of the true position. This is not a particularly difficult test for a RUT to retain signal lock through, as the linear acceleration is about 0.15 g and the centripetal acceleration is about 0.25 g.
It is difficult for independent third parties to carry out a receiver evaluation following 3GPP guidelines as several of the tests require receiver restarts, which in turn requires testing automation. Depending on the receiver-evaluation hardware availability, restart commands may not be available to to an independent evaluator.
3GPP receiver testing results are quoted as pass or fail over a large number of short evaluations. For the dynamic environment, the system performance over continuous time is required to make a proper comparison between evaluated receivers.
In general, evaluating the GPS engines embedded within cell phones or other devices is difficult. Most are not made to interface with an external antenna, and the mere act of adding an antenna connection can significantly alter performance. The output format is not always documented, if it is even available to an end user. To allow fair across-the-board comparisons, GPS chipset manufacturers should make available development kits that have external antenna connections and well-documented message output formats.
Drive-Test Configuration
Current live dynamic testing requires multiple systems to be operating in a moving vehicle (see opening Figure 1). A truth-reference system, usually a high-grade GPS/INS device along with post-processing, provides the basis to which all other RUT are compared. This system requires a dedicated vehicle rooftop antenna with the best possible sky view, separate from a lower-grade test antenna located within the vehicle. Each RUT is connected to the representative consumer-grade antenna located in the vehicle through a high-isolation splitter that suppresses inter-receiver interference. It is important at this point that the gain be set appropriately for each RUT, depending on the front-end expectations while maintaining an equivalent noise figure across all receivers.
Visualization Methods
In addition to quantitative methods, we have created a qualitative visualization to assist with interpretation of the raw data. The same parsed data sets that provide the statistical script input are fed into a viewer script along with the post-processed truth reference data. With the truth-reference system data plotted in the center of the screen, each RUT is then plotted the correct distance and direction away, based on the distance and direction of error compared to truth. The receiver plots are overlaid onto Google Earth images centered on the truth-reference location. Plots of number of satellites utilized (top right of Figure 5) and elevation (middle right) as reported by each receiver and the sampled RF spectrum (lower right) are also included.
For each reporting epoch, based on the data frequency of the truth-reference system, a frame is generated with the aforementioned characteristics. These frames are gathered and encoded into a movie clip which can then be used as a quick and simple qualitative tool for receiver comparison. Figure 5 shows an individual movie frame. A forward-looking camera capability is also being added to this movie so the test environment can be documented from multiple angles.
FIGURE 5. Movie visualization screenshot
While observing this movie, variations in the sampled RF spectrum from interference or blockages can be associated with the current landscape. Locations of RFI sources can be identified and avoided (or included) in future testing. These RFI and significant blockage locations are of interest for receiver RF component and navigation filter development. The next three figures show spectrum snapshots during various parts of a drive test. In Figure 6, the cumulative GPS spectra rises above the noise floor and is visible during open sky conditions. While below ground level, Figure 7 shows only the front-end filter shape (and relatively minor RFI). Figure 8 shows an example of severe RFI when near a specific parking garage location.
FIGURE 6. Open-sky spectrum (centered on 1575.42 MHz)
FIGURE 7. Spectrum while below ground level (centered on 1575.42 MHz).
FIGURE 8. Spectrum near interference source (centered on 1575.42 MHz).
Record/Playback Concept
To overcome the limitations of hardware signal simulators and repeated vehicle drive testing, the RF record/playback testing method is utilized at the university. Commercially available equipment, capable of recording and playing back an RF signal, has recently become available. Equipment options exist for between $10,000–100,000, with 1–16 bit sampling and 4–25 MHz front-end bandwidth.
Figures 9 and 10 show the concept of “record once, playback many times.” During a drive test, the truth-reference system and RF recording system log samples to disk. There is no need for the RUT to be included during the actual drive test.
In the laboratory, the logged RF samples are replayed through a splitter to all RUT. The effect of receiver configuration changes can be evaluated without having to repeat the drive test. At a later time, additional receivers can also be tested using the same stored RF sample file.
During separate record and playback phases, testing considerations and methods discussed previously are implemented.
Since the recording process can only obviously capture current conditions, additional drive-test collections are required if different satellite geometry is desired, or if additional representative antennas need to be evaluated.
Repeatability of RPS Testing
To validate that the playback signal levels were not significantly different from live signals, we conducted an urban, dynamic evaluation. Figure 11 shows that there is typically not more than a 1 dB difference in reported C/N0 between live and playback modes when testing a receiver that only reported integer values. The two dropout instances were excursions into parking garages.
FIGURE 11. Live and playback C/N0 values
Figure 12 compares the navigation statistics between replays, using the same five playbacks as in Figure 11. The playbacks show a 1-sigma horizontal position solution spread under 1 meter for approximately 83 percent of the test.
FIGURE 12. Playback Horizontal Position Error Spread.
These two figures verify the repeatability of the RPS testing method and solidify it as an alternative to both signal-simulator testing and live testing of satellite navigation receivers.
Denver Testing Method
To evaluate the RPS concept, we conducted tests in three locations: Boulder, Denver, and Interstate Highway 70, all in Colorado. The Boulder and Denver locations were urban collections, while the Interstate 70 location was a natural canyon with significant elevation change. The collection at each location was repeated with two different representative antennas (patch and cell phone) at nearly the same sidereal time in order to keep the overhead satellite constellation similar.
We examine here the November 11 and 16 Denver tests. The November 11 test used a patch antenna that places nearly all its gain in the upward direction, making it more immune to interfering sources below and to its sides. Figure 13 shows the patch antenn
a location on the van, as well as the truth-system antenna location utilized for testing on both days.
FIGURE 13. Patch antenna (dashboard) and truth-system antenna (rooftop) locations.
The November 16 test used a cell-phone GPS antenna that does not have a preferential gain direction, making it more susceptible to interfering sources below and to its sides. This antenna type is representative of the typical low-cost antenna (in some cases as simple as a piece of wire) found in consumer cell phones. Figure 14 shows the cell-phone antenna suction-cup mounted to the front window of the testing van. The representative antenna mounting location was chosen to minimize locally-generated RFI effects while also being representative of a typical vehicle-use case.
FIGURE 14. Cell-phone antenna location.
The required equipment and connections are minimal when performing RPS drive testing, as no RUTs are included. The inset to Figure 1 at the beginning of this article shows the RPS unit in the rear of the van, mounted on layers of foam to reduce vibration, which, if not properly addressed, can cause errors in mechanical hard drives writing data at high rates. Also visible are the truth receiver on the center of the van floor, and the car batteries for powering it and the IMU. The IMU is mounted to the vehicle frame and is not shown.
The test drive trajectory through Denver on November 11 and 16 as reported by the truth system is shown in black in Figure 15 and is also repeated in Figures 16 and 17. The test lasted approximately 40 minutes on both days. It started in the upper left part of Figure 15 and continued zig-zagging through downtown to the lower right.
FIGURE 15. Truth trajectory for November 11 and 16 tests.
Figures 16 and 17 show particularly difficult blocks for the four receivers tested under the replay method. These receivers are denoted A (green), B (blue), C (red), and D (yellow).
FIGURE 16. Difficult block #1 during November 11 test and truth system antenna (rooftop) locations.
The horizontal positioning error statistics for two receivers on the November 11 test are shown in Figures 18 and 19. The left side shows horizontal error in two different zoom levels. The right side shows a histogram and cumulative distribution of errors, and several reporting metrics over the entire test. Even though receiver A in general outperformed receiver B, from the error time histories there are noticeable periods where both receivers simultaneously had positioning difficulties.
FIGURE 17. Difficult block #2 during November 11 test.
Table 1 summarizes the horizontal positioning statistics for all receivers during both tests. Positioning accuracy was severely degraded when replaying samples collected with the cell-phone antenna as compared to the patch antenna. Receiver A was the most accurate across both tests, while receiver B was the least accurate. The uncertainty of the truth system was subtracted out when producing the horizontal positioning results for all receivers.
Table 1
Conclusions
The record-and-playback system testing approach, in our opinion, represents the best way to test hardware receivers. It overcomes the fidelity limits of simulator-based testing, especially when considering the difficult-to-model urban environment. During receiver development, it requires only a single drive test for each location, as sampled RF data can be replayed from disk.
Having demonstrated that RPS testing is repeatable, we have produced a library of RF sample files representing real-world conditions for continued receiver development and testing purposes.
Eric Vinande is Ph.D. student at the University of Colorado studying GPS/MEMS inertial sensor integration and urban RFI aspects.
Brian Weinstein is a BSEE student participating in the Undergraduate Research Opportunity Program for GNSS receiver testing at the University of Colorado.
Tianxing Chu is a visiting researcher at the University of Colorado from Peking University where he is a Ph.D. student.
Dennis Akos is an associate professor within the Aerospace Engineering Sciences Department at the University of Colorado with concurrent appointments at Stanford University and Luleå University of Technology.
Manufacturers
Development of the methodology described here used two different RPS systems, one from LabSat (RaceLogic) and one from Averna. The test data come from the Averna system.
