GPS positioning in urban scenarios is challenging because of large numbers of non-line-of-sight outlier measurements. We propose a robust positioning algorithm that combines GPS observations with visual-inertial odometry information to handle such outliers. We demonstrate the effectiveness of our algorithm in a simulation scenario with close to 80-percent outliers. In experiments in a mild urban-canyon environment, our approach reduces the 95th percentile horizontal positioning error by 66 percent compared to a GPS-only solution.
Motivation
GPS performance drastically degrades if large parts of the sky are obstructed. This occurs for example in urban-canyon scenarios, where GPS positions may be off by as much as 50 meters. These large positioning errors are prohibitive in applications such as autonomous vehicles and advanced driver assistance systems (ADAS).The large positioning errors in urban canyons are mainly caused by non-line-of-sight (NLOS) observations and multipath effects. Such observations result when the line-of-sight (LOS) path from the receiver to a satellite is blocked, and the receiver instead erroneously tracks a reflected version of the satellite signal.
Summary of Results
We propose a low-cost method to detect and remove such NLOS outliers by combining GPS pseudorange measurements with visual inertial odometry (VIO) measurements. These measurements are complementary: GPS pseudoranges provide absolute positioning information; VIO measurements, constructed from camera frames and inertial measurements, provide high-accuracy relative positioning.
We develop a robust and efficient, tightly-coupled GPS+VIO positioning algorithm, able to work under extremely challenging conditions. For example, in scenarios with close to 80 percent of GPS measurement outliers or with only intermittent satellite visibility. Even under these extreme conditions, the proposed algorithms are able to produce reliable and accurate position estimates.
Problem Setting
The overall positioning system consists of a GPS module and a VIO module. The GPS module provides raw pseudorange and Doppler range-rate measurements. The VIO module consists of a camera along with inertial sensors such as an accelerometer and a gyroscope. The output of the VIO processing engine are vectors of velocities and displacements expressed in the local camera coordinate frame.
We will not go into the details of the VIO design, rather we will use it as a black box that provides us with the velocities. The goal is to integrate the pseudorange measurements across time using the highly accurate velocities from the VIO to detect and discard the measurements corrupted by NLOS errors.
The positioning algorithm consists of two stages. In the first stage, we transform the velocities from the VIO frame of reference to the GPS frame of reference. This requires estimation of the rotation matrix relating the VIO frame and the GPS frame. Once this transformation is completed, the second stage is to perform outlier detection and to estimate the rover position.
Everyone talks about the weather, but nobody does anything about it — right?
Our lead authors this month are doing something about it.
The July cover story of GPS World magazine was titled “See into the Smoke with Inertial.” This month’s feature could have been called “See into the Fog with CDGNSS,” but we just didn’t have room in the already extensive article to go into that angle. So here it is.
Precise carrier-phase differential GNSS positioning will in the near future become a must-have complement to cameras and lidar for all-weather automated driving. Positioning will be furnished, as the article explains, by a dense reference network broadcasting to low-cost antennas for precise (10 centimeter) performance.
Here’s the kicker, not included in your cover-story package, although hinted at by the orange and green trapezoids on the cover, and replicated in the fog-bound version above.
Such vehicle positioning would enable new driver-assistance systems. With precise knowledge of a vehicle’s position and orientation, intuitive driving directions can be rendered on the windshield in luminous paths that appear to be painted on the roadway. These paths will guide the driver along the fastest route to destination. Other symbols will suggest lane changes for safety or efficiency, and highlight the presence of vehicles dangerously close ahead. Because satellite navigation signals are not affected by rain, snow or fog, they can be combined with radar sensors to safely guide a driver or an automated vehicle in all weather.
As author Todd Humphreys explains it, “Imagine how relaxing it would be to follow a yellow brick road safely home! I envisioned this augmented-reality heads-up display during a recent road trip. Driving on unfamiliar roads, I was trying to interpret various route options on my wife’s smartphone while simultaneously fielding questions (in Spanish!) from my in-laws, and more questions from my nine-year old son. It was too much to ask of one driver!”
Not any more. That is, soon, in our brave new future, no longer.
Rohde & Schwarz’s FSW85 high-end signal and spectrum analyzer, including an analysis option for FMCW chirp signals, is suitable for testing advanced driver assistance systems (ADAS). It analyzes automotive radar sensors designed for designated frequency bands around 24 gigahertz and 79 gigahertz.
It can cover the frequency range from 2 gigahertz to 85 gigahertz in a single sweep. Its optional analysis bandwidth of up to 2 gigahertz makes it possible to demodulate and thoroughly analyze even extremely broadband signals.
R&S also offers an eCall test system consisting of the R&S CMW500 and the GNSS-capable R&S SMBV100A vector signal generator, a hardware-in-the-loop solution for standard-compliant end-to-end tests for wireless communications and GNSS-capable components in in-vehicle systems.
Sponsoredby:NavCom Broadcast date: Thursday, June 18, 2015 On-Demand Available Until: Friday, June 17, 2016 Moderator: Alan Cameron, Editor-In-Chief and Publisher, GPS World Speakers: John Fischer, Chief Technology Officer, Spectracom; Lisa Perdue, Applications Engineer, Spectracom; Hironori Sasaki
Director of Solutions Architecture, Spectracom Summary: Advanced driver-assistance systems (ADAS) are now integrated in all luxury cars and moving into mainstream models. Although no driverless car is expected to operate freely on public roads for the next 10 years, some open test drives have already taken place, including one 100-mile highway cruise by a driverless Mercedes. This technology is currently restrained by legal issues and the lack of reliable nationwide mapping data — but the platforms are nearly ready to go. Join us as we explore the current state of affairs and the likely near-term future developments.
Recent progress with Dedicated Short Range Communications (DSRC) Notice of Proposed Rule Making (NPRM) brings connected cars or V2X — connectivity between vehicles, infrastructure and all road users — closer to reality than ever before. If all goes well, an NHTSA mandate on DSRC in new light vehicles is expected to start around 2020 as a phase-in plan, with completion around 2025.
Regulations for aftermarket devices are expected to come soon after. The mandate is expected to leave auto OEMs to choose the applications and human-machine interface (HMI). This will be the culmination of more than a decade of technology development and standardization by U.S. Department of Transportation (USDOT), automotive OEMs and other industry partners.
Significance of V2X. According to USDOT, V2X technology can positively impact more than 80% of non-impaired vehicle crash types that result in over 30,000 deaths in the U.S. alone. A report by the Federal Highway Administration to Congress states that V2X technology is ready to be deployed in the near future and is expected to yield significant safety and efficiency benefits.
From a consumer’s perspective, V2X will be a part of a vehicle ADAS (Active Safety Driver Assistance System). Initial systems will provide information only, and these systems are expected to evolve into warning and control capabilities. In a future vehicle, information from multiple sensors including V2X will be combined/fused to generate a view of the surrounding environment. Figure 1 gives an example of such sensors including long- and short-range radar, lidar, cameras and V2X. V2X offers unique advantages over other sensors that depend on direct line-of-sight. Information can be received from vehicles not visible to other sensors, giving a much larger field of view. V2X can transmit information directly from traffic control devices, instead of inferring information from camera observations.
Figure 1. Example of a vehicle sensor configuration.
Figure 2 depicts the sensor fusion screen from an ADAS development platform by Renesas Electronics America. Such a platform offers the flexibility to implement an ADAS using all available sensors, for example blind-spot warning from radar, forward collision warnings from combined radar, camera and V2X, surround object detection from combined radar, lidar, vision and V2X, with information presented via an OEM-specific HMI.
Figure 2. Renesas ADAS development platform.
GNSS role and challenges
V2X is built on the assumption that vehicles, infrastructure elements, and other road users are location-aware and can communicate critical information to others around them. As seen in Figure 3, the system will position all communicating V2X entities with respect to the host vehicle and security interface, which validates all relevant DSRC messages. A control area network (CAN) or a similar interface will be needed for direct access to vehicle information such as brake and turn-light status and odometer. Interfaces to long-range connectivity such as cellular networks and other data sources such as maps may also be included. The system will connect to an HMI to display information, and future systems will likely evolve to vehicle control functions.
Figure 3. Components of a V2X system.
Looking at the components of an over-the-air (OTA) V2X basic safety message (BSM), this includes a UTC-based time marker, WGS84-based position, and an estimated position error — all critical data that primarily depend on GNSS. RTCM-formatted data may also be sent as optional attachments. A BSM-like personal safety message (PSM) is also defined for pedestrians with V2X-enabled devices.
As per current Minimum Performance Requirements (MPR), a UTC time source with better than 1 millisecond accuracy is required in a V2X device. While almost all current prototypes use GNSS as source of time, others, such as NTP, may also be used. Accurate time reference is a critical prerequisite for basic DSRC functionality. MPR requires time-marked position estimates with 2D and elevation accuracy of 1.5 and 3 meters or better (1 sigma) under open-sky conditions. The automotive industry has opted to define open sky as unobstructed sky view above 5-degree elevation with seven or more satellites visible with HDOP and VDOP limits. The industry expectation is to use this criteria to select GNSS devices that could eventually support lane-level applications (better than 1.5-meter accuracy).
MPR does not put any requirements on the accuracy of the position error estimate in the BSM. It does require that a vehicle stop transmitting BSM whenever the aforementioned time and position accuracy requirements are not met. This implies that a V2X-enabled vehicle may disappear from the V2X view of others in a dense urban canyon or similar environments, leaving at least two questions for system designers from a GNSS perspective alone. First, how to reliably declare that the system cannot meet time and position accuracy requirements, and second, how to deal with the vehicle itself and other V2X entities that may cease to function or broadcast due to GNSS or other limitations. V2X systems are assumed to include inertial and vehicle sensor integration.
Road Ahead. Starting in 2017, connected vehicle pilots (CVP) in New York, Tampa, Florida, and Wyoming will be the next major milestone for V2X. These deployments will be limited to commercial fleets (taxis, public transit, city/road crews and delivery trucks) and some limited road-user categories.
Among the automotive OEMs, Toyota was the first to offer V2X-based driver-assistance technology as ITS Connect in Japan in 2015. General Motors is the first to announce a V2X technology offering in a passenger vehicle in the U.S. with an initial rollout in select 2017 models. The first phase of V2X deployments will only provide driver assistance information while subsequent iterations are expected to bring in safety-focused functions leading to control capabilities.
There is a growing interest in the cellular industry to support V2X-like communication in an upcoming release of the 3GPP standards commonly referenced as 5G. This would enable low latency, peer-to-peer communication with the advantage of an existing device provisioning/authentication infrastructure, something that needs to be built up for DSRC. However, 5G is still a concept, and judging by the lifecycle of LTE, a 5G deployment will take several years to start and several more years to fully deploy while still leaving some rural areas with legacy technology. A framework to manage commercial traffic vs. likely free safety traffic will also be required. These raise the question as to how 5G alone can support vehicle safety applications nationwide.
The FCC has recently proposed a rule to potentially open up the DSRC band for unlicensed Wi-Fi devices, provided Wi-Fi users do not interfere with the primary safety use. Automotive and wireless industry and other stakeholders are investigating the feasibility of possible co-existence in the future. Among the proposed solutions are the rechannelization of DSRC to use a smaller bandwidth and a mechanism for Wi-Fi devices to Detect-and-Vacate the DSRC band when a safety user is detected.
From a technology point of view, V2X has reached a significant milestone with R&D in various technology areas converging and critical standards being adopted recently. With Toyota V2X offering in Japan and GM V2X commitment in the U.S., customers will have V2X as an option this year, further proof that V2X will be on the roads soon. However, significant further work is needed to address the GNSS accuracy and reliability needed for next-generation systems and to address GNSS-specific vulnerabilities such as jamming or spoofing. The New York CVP, which includes deep urban canyons, will probably be a great opportunity for GNSS and V2X communicates to work together on some of these limitations.
Future automotive GNSS positioning in urban scenarios
By Martin Escher, Mirko Stanisak and Ulf Bestmann
INNOVATION INSIGHTS with Richard Langley
WHERE ARE WE GOING with GNSS positioning? There have been many advances in satellite-based positioning over the past couple of decades and there are more to come.
Probably the most significant advance, affecting the most users, has been the further miniaturization of GNSS chipsets and modules. Virtually every mobile phone now includes a GPS component. Developers have also made these embedded devices more sensitive so that they can work with smaller, less efficient antennas. Furthermore, GPS satellites are now being launched with additional, more capable signals and already high-end receivers are starting to use these signals. Once full constellations transmitting these signals are in place, consumer devices will likely make use of them as well.
Another very important advance in GNSS positioning has been the development of additional GNSS constellations and multi-GNSS receivers capable of using their signals. Actually, it’s been a multi-GNSS world for quite a while now. The Russians began development of GLONASS shortly after work began on fielding GPS and both systems achieved full operational capability in the mid-1990s. Unfortunately, due to financial problems following the break-up of the Soviet Union, the number of operating GLONASS satellites fell to the single digits making the system virtually unusable. However, with renewed government support, GLONASS has once again become a viable GNSS and many consumer and professional receivers can track and use GLONASS signals along with those of GPS.
In the 1990s, we also saw the development of the U.S. Wide Area Augmentation System, transmitting GPS correction and integrity information from geostationary satellites on the GPS L1 (and subsequently L5) frequency. Other compatible satellite-based augmentation systems followed, including the European Geostationary Navigation Overlay Service, Japan’s Multi-Functional Transport Satellite Satellite-based Augmentation System, India’s GPS Aided GEO Augmentation System, and Russia’s System for Differential Correction and Monitoring. Besides enhancing integrity, the data transmitted by the satellites of these systems improves GPS pseudorange-based positioning accuracy, sometimes to below the one-meter level.
Starting about 15 years ago, we have seen the development of additional autonomous GNSSs, joining GPS and GLONASS. The European Galileo system is under construction as is China’s BeiDou system. And although only providing regional coverage, we should also mention Japan’s Quasi-Zenith Satellite System and the Indian Regional Navigation Satellite System. While all of the new systems are still in development and full constellations are still some years away from completion, the signals from the satellites already in orbit can be used to supplement those received from GPS and GLONASS satellites to improve positioning and navigation availability for some difficult navigation scenarios.
One of the most difficult situations requiring a continuous positioning capability is driving in built-up areas where buildings and other objects can block the signals from a number of GPS satellites such that GPS-only positioning becomes impossible. Even if four or more satellites are in view of the satellite navigation receiver’s antenna, those satellites might have unfavorable geometry, resulting in significantly degraded positioning accuracy. However, if the receiver can access the signals of two or more GNSSs, then position fixes might be available where none were possible with GPS alone, and the accuracies of marginal fixes might be improved.
In this month’s column, we take a look at some early work in using multi-GNSS plus additional sensors for navigating in the heart of the city of Braunschweig, Germany (the birth place of Johann Friedrich Carl Gauss, the inventor of least squares and the father of modern geodesy), and how the additional signals can help us to get where we’re going.
In the near future, we will see the introduction of more and more next-generation advanced driver assistance systems (ADASs) targeting the field of automated or autonomous driving. These systems will have to be considered as safety critical. In contrast to conventional localization systems, they will have to guarantee a higher overall accuracy and integrity to their target applications. Of course, the overall performance of any localization system is mostly limited by its behavior during the worst conditions.
Such behavior is a very limiting factor especially for an ADAS that uses a GNSS such as GPS. The accuracy and integrity of GNSS depend on the quality and availability of satellite signals. The more signals that are available, the greater are the accuracy and integrity. However, as GNSS signals can be blocked easily, the worst-time behavior is difficult to characterize, especially in challenging urban scenarios important for an ADAS.
To face these challenges, additional sensors such as inertial measurement units (IMUs) or odometers can be used for positioning as well. These sensors can increase the availability and accuracy for situations where GNSS-based positioning is not available. Additionally, the characteristics of these sensors are complementary to satellite navigation. The combination of these sensors with satellite navigation thus has the potential to achieve a positioning accuracy and integrity superior to that of single-system performance.
As the number of GNSS measurements is crucial for a precise position fix, the parallel use of different GNSS constellations can improve the overall performance significantly.
Four global satellite-positioning systems are now available. The American GPS and the Russian GLONASS have been in operation for years and are already used in a wide variety of applications. Additionally, newer systems like the European Galileo and the Chinese BeiDou systems are under construction. Even though these systems do not have continuous worldwide availability at the moment, their currently available satellites can already be included in multi-constellation GNSS positioning. Once more satellites are in orbit, the benefit of multi-constellation GNSS will increase even further.
In this article, we take a look at the current performance of multi-constellation GNSS positioning in an urban scenario, contrasting it with GPS-only positioning as well as GNSS positioning aided by additional sensors.
Satellites in orbit
To characterize multi-constellation GNSS performance, stationary GNSS data has been collected using different receivers in Braunschweig, Germany. GNSS data from GPS, GLONASS, Galileo and BeiDou was recorded over a 14-hour window on November 20, 2015.
Based on the broadcast ephemeris data and the receiver’s position, the availability of GNSS measurements was calculated for the duration of the campaign. TABLE 1 shows the number of all satellites of the different constellations as well as the minimum and maximum number of available satellites for each system during the recording period down to an elevation angle of 0°.
Table 1. Number of satellites in orbit and in view during a 14-hour window.
FIGURE 1 shows the satellite availability for the measurement campaign. To obtain a position fix using a single GNSS constellation, range measurements to at least four satellites of this constellation must be acquired. Thus, assuming optimal reception of GNSS signals, single-constellation positioning was possible for the full observing window using only GPS, only GLONASS and only BeiDou satellites. On the other hand, Galileo-only position fixes were not possible at any time due to the low number of simultaneously visible satellites.
FIGURE 1. Satellites in view from Braunschweig, Germany.
However, combining measurements from different GNSS constellations in parallel — multi-constellation GNSS — provides the most benefit.
Multi-Constellation GNSS
All major GNSS constellations operate independently and use different reference frames for position and time. To combine measurements of different GNSS constellations, the correct handling of the diverse reference frames needs to be ensured.
On the one hand, the different coordinate systems have to be taken into account. However, the differences between the position frames is usually kept to within a few centimeters and can thus be neglected for most standalone-GNSS applications.
On the other hand, the handling of the different system time scales requires a specific approach. Even though the inter-system biases (that is, the differences between the system time scales) are usually kept within a few nanoseconds, the influence of the inter-system offsets must not be ignored for most applications and have to be taken into account for a combined position solution.
The most common approach is to extend the estimated state vector with a distinct clock error for each used constellation. For a combined position solution incorporating GPS, GLONASS, Galileo and BeiDou, the state vector used for the least-squares estimation could look like this:
. (1)
Each pseudorange measurement only contributes to its respective clock-error component.
Of course, as the values of more unknown variables have to be estimated, the number of necessary GNSS measurements increases, too. To calculate a combined position solution including GPS, GLONASS, Galileo and BeiDou for the above-mentioned example, seven variables must be estimated. This means that at least seven independent GNSS measurements are necessary at each epoch. However, if no satellite of a specific constellation is available, the state vector can also be adapted to not estimate the corresponding clock error. In this way, the availability of a multi-constellation GNSS solution is always higher or, in the worst case, equal to that of the single-constellation GNSS solutions.
By being able to use more than just one GNSS constellation, the geometric distribution of the satellites over the sky is improved, resulting in a lower dilution of precision (DOP). A lower DOP value usually indicates a better mapping of range measurement precision into the position precision. However, as the different GNSS constellations are currently in different states of maturity, the range precision varies significantly. Thus, a multi-constellation position solution is not necessarily more accurate than a single-constellation solution, but will benefit from an improved overall availability and integrity.
Such a capability is particularly important for safe operations in constrained scenarios like urban canyons, which are a common challenge for automotive applications. Compared to currently prevailing GPS-only positioning, multi-constellation GNSS has the potential to enable safety-of-life services, which will require a high level of integrity in the automotive domain.
Tight coupling
To take even greater advantage of multi-GNSS positioning in challenging environments, the combination with additional sensors can improve the overall positioning performance significantly. The Institute of Flight Guidance at the Technische Universität Braunschweig has developed a tightly coupled GPS fusion system, which incorporates measurements of a close-to-market IMU and odometer sensors for reliable urban car positioning.
This system is capable of using raw data from a reference station receiver to generate differential GNSS corrections. These differential corrections must be free from reference-receiver clock error before they can be used by the tightly coupled system (rover-receiver clock-bias update by pseudorange positioning, rover-receiver clock-drift update by Doppler frequency velocity estimation, and clock-bias prediction by clock drift).
. (2)
As shown in Equation 2, the system calculates the residuals for each pseudorange (PSR) received by the reference receiver based on the well-known reference antenna position and the current satellite position as calculated using its broadcast ephemeris . While calculating the residuals, it involves the atmospheric effects ε j computed by the Klobuchar ionosphere delay model and a modified Hopfield tropospheric delay model.
These residuals must be corrected by the satellite clock errors (also calculated using the broadcast ephemeris). The arithmetic average of the corrected residuals is used as an estimate for the reference receiver clock error (see Equation 3). This approach is sufficient for most applications, but it is also possible to use additional algorithms to estimate the clock error more accurately.
. (3)
To generate reference receiver clock error-free pseudorange corrections, the residuals are calculated a second time. Only the estimated clock error of the reference receiver is removed in the second set of residuals:
. (4)
The assumption was made that these residuals correct all satellites, all atmospheric errors and the inter-system time errors.
With this assumption, the tightly coupled system uses the corrected residuals as pseudorange corrections for the ranges measured by the rover receiver. Using the corrected pseudoranges, the tightly coupled system can estimate the rover receiver’s clock error for positioning:
. (5)
In this way, the inter-system offsets are eliminated as well. Corrected multi-constellation GNSS measurements can thus be processed by estimating one receiver clock error only.
Simulation of obstacles
The performance of satellite navigation is affected directly by the distribution of the useable GNSS satellites over the sky. The more GNSS satellites are spread out over the sky, the lower the DOP value and the better the positioning accuracy. For reference, FIGURE 2 shows a sky plot of unconstrained GNSS with perfect reception of all GNSS satellites during the measurement period of 14 hours. Combining the satellites of all four GNSS core constellations (GPS, GLONASS, Galileo and BeiDou), up to 30 satellites are usable at the same time.
FIGURE 2. Sky plot of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) at Braunschweig.
Of course, this is an optimized scenario that can only be achieved using high-quality antennas without any obstacles in the vicinity. Many applications, including urban automotive situations, do not have a comparable reception of GNSS data, and will suffer from blocked satellites and multipath reception.
Therefore, we created a simulation of surrounding obstacles to predict the behavior of GNSS positioning in challenging urban scenarios. In this simulation, all buildings are represented by endless walls with constant height. A satellite is assumed to be invisible if its line of sight crosses the wall.
To get a first impression of the usability of this approach, we took GNSS measurements in front of the Institute of Flight Guidance in Braunschweig.
Using this scenario, the same simulation of optimal visibility using ephemeris data has been conducted again. As shown in FIGURE 3, large portions of the sky are blocked by the simulated obstacles.
FIGURE 3. Sky plot with valid (thick lines) and invalid (thin lines) measurements.
Of course, the blockages also affect the number of visible satellites as shown in FIGURE 4. Instead of 23 to 31 satellites for the unconstrained scenario, only 11 to 18 satellites are now visible.
FIGURE 4. Comparison of satellite visibility with and without simulated obstacles.
In a following step, we validated the theoretical predictions of the visible GNSS satellites against the reception by a GNSS receiver of the available signals at the simulated position.
Validation of simulation
For a validation of the obstacle simulation, data from a high-grade receiver was used for the validation of the simulation. This modern GNSS receiver is able to track signals from all GNSS constellations (GPS, GLONASS, Galileo and BeiDou) on different GNSS frequencies with a data rate of up to 100 Hz. The BeiDou reception, however, was only acquired recently before the recording of the data and unfortunately suffered from bad BeiDou tracking performance.
The receiver was connected to a multi-frequency antenna. This GNSS antenna was installed at the back of the roof of the research car. A sky plot of the tracked signals is shown in FIGURE 5.
FIGURE 5. Tracked signals of the high-end receiver.
A comparison of the simulated (Figure 3) and the actual (Figure 5) sky plots shows a very good agreement between the simulations and the measurements. There are, however, some spots in the sky plot where the real GNSS receiver is able to track satellites that are behind a building. This can be explained by the reception of signals through the windows of the building. Thus, the signal-quality indication based on the receiver’s signal-to-noise measurements of these spots is quite bad in these situations.
As described before, we experienced some problems with the BeiDou reception of the high-grade receiver. Thus, we used an additional single-frequency GNSS receiver. This receiver is capable of providing raw L1 GNSS data of two constellations simultaneously and was configured to track GPS and BeiDou satellites. In this way, an additional sky plot showing GPS and BeiDou reception in the same setup could be generated. The visible BeiDou satellites are shown in light blue in FIGURE 6 and are in accordance with the simulated visibility.
FIGURE 6. Valid signals sky plot of the single-frequency receiver data.
In general, the sky plots identify significant differences compared to the simulated ones as even in regions blocked by buildings some satellites can still be tracked. The contour of the building, however, can still be seen in the signal strength plot in FIGURE 7.
FIGURE 7. Signals strength sky plot of the single-frequency data.
This result is an indication that the single-frequency receiver can track some satellites blocked by the buildings using diffracted or reflected signals, but, of course, resulting in worse positioning accuracy.
It goes without saying that the various receivers we used are designed with contrary goals in mind. High-performance GNSS receivers are optimized to provide accurate position solutions for high-demanding applications. Thus, the receiver attempts to suppress multipath effects as much as possible to obtain precise and accurate position solutions. The single-frequency receiver, on the other hand, is closer to the low-price, high-volume class of receivers for portable devices, and is optimized to provide valid position output even in challenging environmental situations. Thus, the receivers must not be compared directly because they are designed for completely different purposes.
Simulating urban canyons
To assess the overall multi-GNSS performance in urban scenarios, we conducted driving tests in the city center of Braunschweig. Driving through city centers is particularly challenging for any positioning algorithm because of various potential sources of errors. Instead of only using suburban commuter roads, the route we chose represents the most challenging situations for the city center. Most of the roads are surrounded by multi-story buildings (typically up to six floors) very close to the driving lanes. This is – especially for European cities – a common and challenging urban scenario for satellite navigation. An example of such a scenario is shown in FIGURE 8.
FIGURE 8. Dimensions of representative urban scenario.
To quantify the impact of the limited GNSS availability due to buildings and other obstacles, we simulated a scenario with walls on both sides of the road. With the road running in a north-south direction, we simulated buildings within a distance of 14 meters and a height of 15 meters. The simulated effect on a GNSS receiver in the middle of the street due to blocked satellites in this scenario is shown in FIGURE 9. Satellites with an elevation angle of up to 65° can be obstructed by the buildings.
FIGURE 9. Sky plot for obstacle simulation of urban canyon.
In this scenario, more than half of the sky is blocked by buildings, making satellite navigation quite challenging. Additionally, Braunschweig is located at about 52° north latitude and is close to the inclination of most GNSS constellation orbits (GPS 55°, Galileo 56°, BeiDou MEO 55°). Only GLONASS satellites can be seen in the far northern part of the sky from time to time due to their inclination of 65°.
Using GPS satellites only, fewer than four satellites are available for long periods of time. On the other hand, using a combination of all constellations, up to 14 satellites can be used even for this constraining scenario. Most of the time, at least seven satellites are visible, allowing a multi-constellation GNSS position solution.
Downtown positioning
To assess the practical benefit of multi-constellation GNSS in urban scenarios, we conducted a test drive in downtown Braunschweig using our research car. This area is dominated by narrow roads with multi-story buildings on both sides of the road. Using recorded data from different GNSS receivers and other sensors, multiple positioning solutions were obtained by post-processing the recorded data to compare the different positioning performances.
As a baseline for comparison, a GPS-only position solution was calculated. This result represents the current state-of-the-art navigation systems for most production cars. All valid GPS-only position fixes are shown in FIGURE 10. For large portions of the test drive, no GPS-only position solution was possible because of insufficient GPS measurements.
FIGURE 10. GPS-only standalone positioning fixes for test drive in Braunschweig.
To quantify the benefit of multi-constellation GNSS compared to GPS-only, a combined position solution was calculated using the same data as before. There was a significant improvement in the availability compared to the GPS-only position solution.
However, even when using multiple GNSS constellations, some areas with no valid GNSS fixes still exist. The GNSS availability can be improved further by using differential corrections from a GNSS reference receiver. The correction data is available in the research car using 4G mobile telecommunication links to different service providers. Each provider uses a network of GNSS receivers to calculate differential corrections. However, all commercially available services are currently limited to GPS and GLONASS. Thus, another stationary multi-constellation GNSS reference receiver at the Institute of Flight Guidance generated correction data for the test drives. As the baselines are short in this scenario (not longer than 10 kilometers), no significant spatial decorrelation is expected.
As the majority of possible inter-system offsets are already eliminated using the differential corrections of identical receiver types, a multi-constellation solution can be calculated here even with as few as four GNSS satellites in view. This is shown in FIGURE 11. In this way, the achieved availability increased again.
FIGURE 11. Differentially corrected multi-constellation positioning fixes for test drive in Braunschweig.
Finally, using all the information available in the car, a hybrid position solution based on differentially corrected GNSS, inertial navigation and the test vehicle’s odometer has been calculated.
In sections without any GNSS positioning aiding (marked red in FIGURE 12), the inertial navigation system was used in dead-reckoning mode. As these outages lasted only for short periods of time, the accuracy of the combined position remained usable for the duration of the test. In this way, an accurate position solution could be calculated for the whole test drive using this tightly coupled positioning algorithm.
FIGURE 12. Tightly coupled positioning trajectory for test drive in Braunschweig.
With increasing positioning complexity, the computational burden increased as well. For a tightly coupled system integrating the measurements of the different sensors, significantly more calculations must be performed in real time than for current GPS-only standalone positioning. However, even today these computations can be easily made using embedded devices.
Conclusions and outlook
For this article, the achievable positioning performance of multi-constellation GNSS has be analyzed with a special emphasis on urban automotive applications. Simulations of constrained environments have been compared with real data and show good agreement. Multi-constellation GNSS outperforms GPS-only positioning, especially in situations where large portions of the sky are blocked by obstacles, because significantly more satellites remain usable. Multi-constellation GNSS has thus the potential to be an important part of future safety-of-life positioning and navigation applications.
However, a few challenges still exist. Some GNSS constellations have not reached their full operational capabilities as not all satellites are in orbit yet (Galileo and BeiDou). Additionally, the ranging errors of these systems are expected to decrease with improved navigation message accuracy and receiver performance.
The availability of numerous GNSS constellations results in new requirements for the receivers as well. Even though most manufacturers of GNSS equipment already support the additional systems with some products, the majority of currently used GNSS receivers is limited to one or two constellations, especially in mass-market applications. In addition, the reception quality of the newer systems is not always on the same level as GPS or GLONASS because of the limited experience that manufacturers have with Galileo and BeiDou. This, we hope, will change in the near future.
Acknowledgments
This article is based on the paper “Future Automotive GNSS Positioning in Urban Scenarios” presented at The Institute of Navigation 2016 International Technical Meeting, held in Monterey, Calif., Jan. 25–28.
Manufacturers
The high-grade receiver used in our tests was a Septentrio AsteRx3. The receiver was connected to a NovAtel GPS-703-GGG antenna. The single-frequency receiver we used was a u-blox LEA-M8T GNSS receiver with firmware version 2.3. Additionally, we used a NovAtel OEM6 multi-GNSS receiver and an Analog Devices ADIS16375BMLZ IMU.
MARTIN ESCHER holds a Dipl.-Ing. in electrical engineering from the Technische Universität (TU) Braunschweig in Braunschweig, Germany, and has been employed as a research engineer at the Institute of Flight Guidance (IFF) since 2010.
MIRKO STANISAK is a research assistant and Ph.D. candidate at the IFF of TU Braunschweig. He received his Dipl.-Ing. in mechanical engineering in 2009 and since then has worked on various GNSS-related topics.
ULF BESTMANN received his Dr.-Ing. in mechanical engineering from the TU Braunschweig in 2010. He is employed at the IFF of TU Braunschweig, where he is head of the navigation department.
Further Reading
Authors’ Conference Paper
“Future Automotive GNSS Positioning in Urban Scenarios” by M. Escher, M. Stanisak and U. Bestmann in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 836–845.
Multi-Constellation GNSS Measurements
“Precise Point Positioning with Galileo Observables” by R.M. White and R.B. Langley in Proceedings of the 5th International Colloquium on Scientific and Fundamental Aspects of the Galileo Programme, Braunschweig, Germany, Oct. 27–29, 2015.
“Accuracy and Reliability of Multi-GNSS Real-Time Precise Positioning: GPS, GLONASS, BeiDou, and Galileo” by X. Li, M. Ge, X. Dai, X. Ren, M. Fritsche, J. Wickert and H. Schuh in Journal of Geodesy, Vol. 89, 2015, pp. 607–635, doi: 10.1007/s00190-015-0802-8.
“Performance Evaluation of Integrated GPS/GIOVE Precise Point Positioning” by W. Cao, A. Hauschild, P. Steigenberger, R.B. Langley, L. Urquhart, M. Santos and O. Montenbruck in Proceedings of ITM 2010, the 2010 International Technical Meeting of The Institute of Navigation, San Diego, Calif., Jan. 25–27, 2010, pp. 540–552.
“A GPS/Galileo Tightly-Coupled Localization System for Safety-Relevant Automotive Assistance Systems” by H.-G. Büsing, M. Escher, T. Scheide and P. Hecker in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Ore., Sept. 19–23, 2011, pp. 356–362.
Geometry Effects on GNSS Positioning
“Dilution of Precision” by R.B. Langley in GPS World, Vol. 10, No. 5, May 1999, pp. 52–59.
Mobileye, a developer of vision and data analysis for Advanced Driver Assistance Systems (ADAS) and autonomous driving, has introduced a new mapping technology development called Road Experience Management (REM).
REM enables crowd-sourced real-time data for precise localization and high-definition lane data that forms an important layer of information to support fully autonomous driving.
Mobileye is engaged with General Motors to integrate REM into existing program launches in an expedited timeframe, as part of GM’s heightened partnership with Mobileye. In addition, on Jan. 5, Mobileye signed a Memorandum of Understanding with Volkswagen and announced a strategic partnership to explore and integrate REM into Volkswagen’s fleet.
The technology is based on software running on Mobileye’s EyeQ processing platforms that extracts landmarks and roadway information at extremely low bandwidths, approximately 10 kb per kilometer of driving. Additionally, backend software running on the cloud integrates the segments of data sent by all vehicles with the on-board software into a global map.
“We leveraged advanced artificial intelligence, used for creating environmental models from camera input, in order to create maps based on local coordinate systems while requiring very low bandwidth,” said Prof. Amnon Shashua, co-founder, chairman and Chief Technology Officer of Mobileye. “The low bandwidth of the model, and the fact that it requires only a camera, which is already available in most new car models as part of the trend towards growing driver assistance deployment, enables the map creation and update to be managed by a cooperative crowd sourcing mechanism.”
A third OEM customer of comparable size is expected to be announced later this year.
Shashua discussed the future of autonomous driving and road mapping at the Consumer Electronics Show in Las Vegas in January.
At CES in Las Vegas, HERE unveiled the HERE HD Live Map, an advanced cloud-based map asset commercially available for vehicles today. HERE is demonstrating HD Live Map at CES: Central Plaza, Booth #CP-2.
Ready to be deployed in connected vehicles in North America and Western Europe, HD Live Map creates a highly detailed and dynamic representation of the road environment, enabling a vehicle to effectively “see around corners” beyond the reach of its on-board sensors.
HD Live Map is an integrated offering, consisting of multiple layers of data delivered in a map-tile format. It is designed to enhance both Advanced Driver Assistance Systems (ADAS) and automated driving functionality, and therefore make driving more comfortable and enjoyable.
HD Live Map includes data which tends to have high permanency, such as lane level information; data which is temporal in nature, such as road construction, traffic and accidents; and analytics data, including speed profile information that informs the vehicle about how to drive based on actual human behavior data.
With highly automated driving set to become prevalent in the next few years, the immediate next step for the automotive industry is to capitalize on the new generation of ADAS that leverages wireless network connectivity and the cloud.
With HERE HD Live Map, automakers have the ability to enhance a vehicle’s ADAS functionality — such as adaptive cruise control, adaptive headlights and curve speed warnings — by giving it access to more accurate and more reliable near real-time content and contextual information about its environment. In doing so, the industry can help drivers build the prerequisite trust and familiarity they need to feel comfortable with increasing levels of vehicle automation.
“As we move towards higher levels of vehicle automation, drivers need to feel that their car is making the right decisions on their behalf,” Floris van de Klashorst, HERE’s vice president of automotive. “When it comes to trusting your car, having consistent real-time awareness of road conditions near and far is absolutely critical. With HD Live Map serving this need, we believe it will become the car industry’s most intelligent vehicle sensor.”
Self-maintaining map. HERE HD Live Map is the first map from HERE that is self-maintaining: through multiple modes of sensor aggregation and ingestion the vehicle’s map is updated and delivered in near real-time.
For example, if vehicle sensors detected a speed limit sign which is inconsistent with what is currently in the map, the map would update accordingly so that other vehicles driving approaching the same spot have the new, correct information. This is important for ADAS functionality such as adaptive cruise control.
Similarly, if a new lane closure was reported, the map would update accordingly so that other vehicles approaching the area can already prepare to switch lanes or alternatively re-route if traffic is heavy.
HERE HD Live Map delivers connected ADAS content via layered live tiles, with dynamic traffic flow data, real-time incident reporting and speed profile data derived from rich behavior information.
HD Live Map is also data-efficient, requiring a small data footprint, with new events able to be layered on the map without the need to update the whole map itself. The small file sizes within each live tile make the delivery of highly precise data much leaner, thus reducing bandwidth requirements.
In the near-term, HD Live Map utilizes a variety of data gathered and delivered by the HERE location platform to enhance the vehicle and the driver’s awareness of what’s happening on the road.
As vehicle automation increases in the future, HD Live Map is ready to serve as an agnostic location cloud, ingesting, aggregating and delivering in near real-time ever vaster quantities of data produced by a variety of sources, especially vehicle sensors. For example, HERE is exploring further enriching its platform with new sensor data from Audi, BMW and Mercedes-Benz vehicles, which would benefit all automakers deploying HD Live Map.
HERE has already been providing either parts or full specifications of HD Live Map for automated driving testing purposes to more than ten automotive companies. Many of those have taken advantage of HD Live Map data HERE is offering of specific stretches of open road in Silicon Valley and Michigan in the United States, as well as in France, Germany and Japan.
Now, with HD Live Map offered across key regions, HERE is able to support automakers seeking to widen and deepen their automated driving development efforts. In supporting larger testbeds, HERE intends to continue to refine HD Live Map together with automakers to ensure it is optimized for their needs today and tomorrow.
“Highly-detailed map data is not only very useful but a requirement for full-featured automated and autonomous driving. In the near term, highly-detailed map data will enhance the performance and benefits of current-generation driver assist technologies; over the longer term, they will enable more effective and efficient operation of vehicles altogether by drivers or self-driving cars. Adding a feedback loop to continually gather, update and share the latest road data will further elevate the technology’s potential,” analysts at IHS Automotive said.
Bluesky has completed a multi-million pound aerial mapping project to assess the impact of vegetation on the electricity network of East Anglia and the South East of England. Working on behalf of UK Power Networks, Bluesky undertook the largest ever combined laser mapping and aerial photography survey commissioned by an electricity distribution network operator in the UK — some 34,000 square kilometers.
The laser mapped (LiDAR) data and aerial photographs were then analyzed to assess the proximity of vegetation to the overhead power lines in order to create a proactive three-year vegetation management program. Bluesky worked in partnership with ADAS, an agricultural and environmental consultancy, to complete the project.
Dedicated survey planes equipped with a lidar mapping system and aerial survey equipment flew the whole of the South East and East of England. Capturing millions of individual laser mapped height measurements and approximately 310,000 aerial images in just over three months, Bluesky successfully completed the unprecedented data capture element of the project within tight project deadlines, in challenging weather conditions and in adherence with strict Air Traffic Control restrictions.
The 80 terabytes of raw data was then processed and analyzed to identify which overhead line spans had vegetation infringement; for example the length of vegetation infestation along each span and its location and distance from the overhead line.
This information has now been incorporated into a 3D web portal that can be viewed from the desktop, enabling UK Power Networks employees to carry out virtual patrols of the network, saving time and reducing the risk of foot patrols, sometimes across difficult terrain including physical barriers such as rivers, ditches, livestock and numerous other potential hazards.
“This innovative £2.5 million project is of immense benefit to our customers and to the company,” said Nigel Hall, head of service development at UK Power Networks. “The risk-based tree-cutting program will help reduce tree-related power cuts for customers, with the additional benefit that it could be carried out without any disturbance to local landowners because it was done from the air rather than on foot.
“As a company it will help us get best value from our £19 million annual tree cutting budget, and the web portal will mean staff can carry out ‘virtual patrols’ from their desk, saving them time and reducing the potential hazards if they had had to walk the lines themselves.”
“Prior to commissioning the LiDAR and aerial mapping project, UK Power Networks undertook regular manual surveys as part of its assessment of network resilience, but the capture of LiDAR and associated aerial photography for the entire catchment area allows for evidence based decision making and long term planning, and provides a proven solution for other network operators,” added Rachel Tidmarsh, managing director of Bluesky.
Roy Dyer, Head of Arboriculture in ADAS and manager of the ADAS contribution to this contract said, “This has been a ground breaking contract. The combination of Bluesky’s technical ability and ADAS’ consultancy experience in managing vegetation near overhead lines enabled us to successfully deliver this challenging contract and improve the management and resilience of the overhead lines owned by UK Power Networks.”
Advanced driver-assistance systems (ADAS) are now integrated in all luxury cars and moving into mainstream models. Governments are getting involved to prevent accidents and minimize the related economic impacts with them. Manufacturers are not far behind; every one of them wishes to be seen as a technology master. Most car and truck companies are working actively on qualifying fully driverless technology today. The military also has a high interest in this area, and has developed autonomous convoy capability for large trucks and supply vehicles.
Although no driverless car is expected to operate freely on public roads for the next 10 years, some open test drives have already taken place, including one 100-mile highway cruise by a driverless Mercedes. This technology is restrained by legal issues and the lack of reliable nationwide mapping data — but the platforms are nearly ready to go.
Join us as we explore the current state of affairs and the likely near-term future developments.
Speakers:
John Fischer, Chief Technology Officer, Spectracom
Fischer has more than 30 years experience creating navigation and communications systems, received his Masters in electrical engineering from SUNY at Buffalo and has worked in radar, command and control, and wireless systems prior to joining Spectracom. To learn more, visit www.spectracom.com.
Lisa Perdue, Applications Engineer, Spectracom
Perdue is an applications engineer at Spectracom and a specialist in GNSS simulation. She has more than 15 years of navigation and RF systems experience, including 10 years of Naval Service.
Topics:
Accurate positioning of ADAS vehicles on the test track using similar methods as used in military UAVs – John Fischer
GNSS and Hybrid Navigation Testing Issues for ADAS and Driverless Cars – Lisa Perdue
Realtime Testing Issues for V2V and V2X for ADAS and Driverless Cars – John Fischer
High-Precision GNSS Needs Help for Continuous Localization Reliability
By Siamak Akhlaghi
Automotive safety and comfort functions, known as Advanced Driver Assistance Systems (ADAS), have become an essential part of modern vehicles. These functions assist drivers in the driving process, providing capabilities such as adaptive cruise control or highway driving mode. To achieve a desired level of performance, the position of the vehicle must be known. Precise positioning supports the vehicle’s systems with planning, executing and monitoring of a particular maneuver.
Position determination, or localization, is the estimation of the location, heading, velocity and acceleration of a vehicle with respect to a fixed coordinate system. High-precision GNSS provides an excellent, worldwide, absolute position reference for localization. However, GNSS technology alone has limitations that must be overcome to make it suitable for use in autonomous systems. For instance, GNSS signals may become blocked or lost due to: obstructions such as in urban canyon or tunnels; multipath, where signals are reflected off the vehicle body; or signal interference from other RF signal sources.
Siamak Akhlaghi
GNSS correction data and data from other sensors on the vehicle can be used to improve the accuracy and reliability of the vehicle localization solution both globally and with respect to the local environment. To achieve the localization performance, accuracy and integrity required for autonomous vehicles, a multi-system, sensor fusion approach seems to be the most promising. Localization systems will require absolute positioning references like precision GNSS as well as local or relative positioning inputs from inertial sensors, odometers, radar, LiDAR, cameras, infrared and ultrasound sensors. It is clear that no single technology will make highly automated driving possible. Rather, the fusion of the entire vehicle’s sensing technologies will provide the localization accuracy and reliability required.
Achieving Accuracy and Reliability with GNSS
GNSS has revolutionized localization in many applications, from precision survey to agricultural guidance. For autonomous driving applications, localization accuracy of 30 centimeters (cm) or less is required. The single-frequency, auto-grade GNSS receivers that have been used in vehicles up to now cannot achieve this level of accuracy. Multi-frequency GNSS receivers utilizing Precise Point Positioning (PPP) correction techniques can achieve accuracies better than 10 cm. PPP algorithms combine GNSS satellite clock and orbit correction data from a global reference station network with high precision GNSS receiver satellite observations to yield robust sub-decimeter positioning without the need for local base stations. Since the PPP corrections can be delivered via satellite, the solution is ideal for highly automated driving where communications infrastructure is costly and in some areas may not be available. Recent advances in PPP techniques provide robust positioning and the ability to quickly regain full accuracy following a temporary loss of GNSS signals, for instance under foliage or highway overpasses.
Figure 1. High-precision / localization with sensor fusion.
Sensor Fusion
Occasional instantaneous irregularities and temporary outages of GNSS can be compensated for by incorporating measurements of the vehicle motion from inertial sensors mounted in the vehicle. An advantage of a tightly coupled GNSS-inertial solution is that the low frequency errors inherent to inertial sensors can be compensated for and removed from the solution. As a result, sensor fusion algorithms provide a highly robust and stable localization solution at data rates as high as 200 Hz. Other sensors in the vehicle, such as odometers, cameras or LiDAR, can also give information about the relative motion of the vehicle and can add to the redundancy, reliability and stability of the localization solution.
Figure 2. With a tightly coupled GNSS-inertial solution, low-frequency errors can be removed from the localization solution. The brown dots are the GNSS solution, the blue dots are the inertial solution, and the combined colors represent the tightly coupled solution.
High-Precision GNSS Antenna
Antennas play a critical role in achieving precise localization with GNSS. While GNSS antenna requirements differ depending on the application, ideally the antenna should receive only signals above the horizon, have a known and stable phase center that is co-located with the geometrical center of the antenna, and have perfect circular polarization characteristics to maximize the reception of the incoming signals. Highly automated driving applications demand high performance as well as compact size and strong interference rejection. Achieving the required performance amidst these challenging constraints will require innovative new GNSS antenna designs.
Autonomous driving will be a reality in the not-too-distant future. Innovation in the suite of sensors and fusion algorithms used for solving the localization challenge will be paramount to making safe and reliable autonomous vehicles. Further, innovation developed for automotive autonomy will support new autonomous vehicle applications in other segments.
High-precision antennas are key.
Siamak Akhlaghi is segment manager for Autonomous Systems at NovAtel. He has 20 years of professional experience working for high-tech sectors with broad experience in inertial sensors and navigation systems.
Geotab, a telematics engineering company, has released its IOX-CAN extender, a plug-and-play solution that allows partners to send data from their device over a private CAN network in the vehicle, supporting integrations on all vehicle types.
Geotab is exhibiting at Mobile World Congress 2015, at Hall 3, Stand 3J20.
According to Geotab, Fortune 500 companies, including 40 percent of the top ten fleets and 18 percent of the top 100 fleets in North America, rely on Geotab’s solutions to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety, and achieve stronger compliance to regulatory changes.
A number of companies are already leveraging the IOX-CAN extender to send data from their devices to the MyGeotab system. Geotab’s integration with Mobileye, a technology company that develops vision-based Advanced Driver Assistance Systems (ADAS) providing warnings for collision prevention and mitigation, has been upgraded with the IOX-CAN extender allowing full support for all vehicle types, including OBDII (on-board diagnostics II) vehicles.
Geotab’s new add-on solution allows Mobileye devices to plug into Geotab’s GO6 and GO7 devices, allowing Mobileye data to be sent to the MyGeotab software platform, where it can be viewed and analyzed by dispatchers and fleet managers.
“Our solutions are designed to make the roads safer for everyone as the issue of distracted driving continues to be a problem,” said Elad Serfaty, vice president and general manager of Mobileye Aftermarket. “Working with Geotab allows us to not only provide feedback to the driver, and with managers who can effect change where needed, but we can do this across all vehicle types.”
“Expanding our integration capabilities and continuing our work with Mobileye is a natural evolution as more and more companies realize the benefits of fleet management,” said Neil Cawse, CEO, Geotab. “Providing collision warnings to drivers just before a crash is the first step to creating a safer driving environment for everyone.”
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