QantasLink, Australia’s largest regional airline, has selected the FreeFlight 1203C SBAS/GNSS sensor for retrofit into its DHC-8-200/300 series of aircraft.
QantasLink paired the 1203C with the Dash 8’s TDR-94D Mode-S transponders.
“We’re pleased to demonstrate once again that there are practical ADS-B solutions for aircraft that have been in service for a while,” said Pete Ring, FreeFlight Systems’ director of Sales and Marketing. “We are proud to supply QantasLink with a straightforward, retrofit solution that extends the life of their DHC-8 fleet.”
As a certified ADS-B position source approved for all ICAO jurisdictions, the integrated 15-channel 1203C SBAS/GNSS sensor is part of a fully rule-compliant ADS-B Out system when paired with a compatible certified Mode S transponder like the TDR-94D. Providing reliable service to fleets worldwide, the 1203C also serves as the approved position source for CPDLC, TAWS/FMS, RNP and other NextGen applications.
Designed for business, regional, airline transport and heavy rotary wing aircraft, the 1203C provides state-of-the-art aviation GPS technology in a proven package. The 1203C allows customers to take advantage of the benefits of NextGen without the need for extensive and costly avionics upgrades.
Founded in 2001 and based in Texas, the company pioneered the first certified aviation WAAS/GPS receiver and the first rule-compliant UAT ADS-B system. FreeFlight Systems designs and manufactures high-performance avionics for flight safety. The solutions deliver substantial safety, cost, environmental and other benefits from the NextGen airspace transformation.
The March 23 program at the Crowne Plaza Hotel, San Francisco Airport, focuses on a fast-changing landscape where automakers and Silicon Valley technology companies are crafting and beginning to roll out their strategies for the autonomous car. Keeping on top of the latest technology, early adoption trends, worldwide markets, liability factors and regulation will be critical in a sector previously known for long product design cycles.
Key topics addressed: Advanced driver assistance systems (ADAS) and autonomous vehicle technologies; solving the high cost of rolling out autonomous systems; investment approaches; testing; innovative players; consumer expectations; market-sector differentiation and strategies to exploit them; the regulatory picture.
Paul Drysch, global director, Connected Car for Jasper Wireless, is the conference chair. Panel moderators include Steve Wollenburg, co-founder and vice president, Business Development, Automatiks; Phil Magney, founder & principal, Vision Systems Intelligence; Derek Kerton, founder, The Kerton Group, Telecom Council, Autotech Council; Jan Hellaker, program director, DRIVE SWEDEN; and Adrian Pearmine, national director for smart cities and connected vehicles, DKS Associates.
The conference also features a reception on the evening before, a hosted luncheon, and a post-program exhibit and reception.
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.
u-blox has released new firmware, FW 3.01, for its u-blox M8 concurrent multi-GNSS platform.
u-blox M8 FW 3.01 now also supports Galileo, in addition to GPS, GLONASS, BeiDou, QZSS and SBAS. It can track up to three constellations concurrently and makes use of all SBAS and QZSS augmentation systems at the same time.
With Galileo fully deployed, the European positioning system will provide access to 24 additional satellites, significantly increasing availability of GNSS signals and further improving position accuracy in challenging urban environments. u-blox M8 supports Galileo-based eCall, the European emergency call system, which will be required in new vehicles starting 2018. u-blox M8 is also compliant with ERA-GLONASS, eCall’s Russian equivalent.
In addition, with FW 3.01, u-blox M8 now boosts the BeiDou acquisition sensitivity and adds support to the Indian GAGAN augmentation system.
u-blox M8 chips and modules are able to operate reliably in difficult environmental conditions as well as in a security attack scenario. Because a growing number of wireless systems rely on GNSS positioning, the threat of attacks, such as diversion of drones or hijacking of car electronics, has become very real.
Security mechanisms are now embedded in FW 3.01, the result of years of intense research at u-blox R&D labs. An anti-spoofing feature detects fake GNSS signals, and a message integrity protection system prevents “man-in-the-middle” attacks. Yet another security function detects and suppresses jamming. Since all this functionality is already built into u-blox M8 FW 3.01, these security mechanisms are a lot more effective than an external system implementation.
Automotive-grade u-blox M8 products benefit from an extended operating temperature of -40 to +105°C and are AEC-Q100 Grade 2 qualified. The extended temperature range allows more flexibility in vehicle integration, such as by integrating a u-blox M8 GNSS receiver into a roof-top antenna where temperatures can reach 105°C.
Another feature of FW 3.01 is the 10 percent power reduction compared to earlier firmware versions of u-blox M8.
The u-blox M8 platform supports applications where navigation performance, reliability, and high accuracy are paramount, whereas the recently announced u-blox 8 platform addresses power sensitive applications such as wearables. u-blox M8 and u-blox 8 products are pin- and software compatible.
Firmware to upgrade existing flash-ROM based u-blox M8 products can be downloaded from the u-blox website. Products with FW 3.01 in ROM will become available in Q2′ 2016.
The U.S. Coast Guard issued a safety alert on Jan. 16, warning mariners of the potential detrimental impact to navigation caused by GPS interference or jamming. The warning emphasizes the importance of understanding how vessel equipment could be impacted by the loss of a GPS signal.
The Coast Guard states that this past summer, multiple outbound vessels from a non-U.S. port suddenly lost GPS signal reception. The net effect was various alarms and a loss of GPS input to the ship’s surface search radar, gyro units and ECDIS, resulting in no GPS data for position fixing, radar over ground speed inputs, gyro speed input and loss of collision avoidance capabilities on the radar display.
Fortunately, the vessels were able to safely continue theirvoyage using radar in heads up display, magnetic compass and terrestrial navigation. Approximately six nautical miles later, the vessels’ GPS units resumed operation. Although the vessels had back-up systems to allow a safe transit, the consequences could have been severe, warns the Coast Guard.
Full content of the alert appears below.
Global Navigation Satellite Systems – Trust, But Verify Report Disruptions Immediately
Do you know what equipment relies upon the U.S. Global Positioning System (GPS) signal? How would you respond if you lost the signal? This past summer, multiple outbound vessels from a non-U.S. port suddenly lost GPS signal reception. The net effect was various alarms and a loss of GPS input to the ship’s surface search radar, gyro units and Electronic Chart Display & Information System (ECDIS), resulting in no GPS data for position fixing, radar over ground speed inputs, gyro speed input and loss of collision avoidance capabilities on the radar display. Fortunately, the vessels were able to safely continue their voyage using radar in heads up display, magnetic compass and terrestrial navigation. Approximately 6nm later, the vessels’ GPS units resumed operation. Although the vessels had back-up systems to allow a safe transit, the consequences could have been severe. These types of events highlight the potential detrimental impact to navigation caused by GPS interference or jamming and the importance in understanding how your vessel’s or facility’s equipment could be impacted by a loss of GPS signal.
Whether walking through the city, driving across town or navigating the world, Global Navigation Satellite Systems (GNSS) have become an integral part of everyday life. However, at times, the positioning signals may be impacted by interference from both natural and human-made sources. The most common types of interference are reception issues, usually due to bad installations, poor antenna positioning or faulty equipment. Jamming devices, while illegal in the U.S. and a threat to safety, have been used for nefarious or deceptive purposes. Interference can also be unintentionally caused when operating GNSS in close proximity to other radiating devices, such as amplified TV antennas (see our Safety Alert 11-02). Therefore, it is important to remember to use all available means for navigation and maintain proficiency so you can still navigate should your primary GPS fail.
Indicators of positioning systems interference include an intermittent signal, no signal, or an incorrect signal. Suspected or suspicious disruptions should be reported immediately. Critical information to take note of during a disruption event includes location, time, and period of outage.
Commercial operators are reminded, should your navigation or other equipment onboard (e.g. AIS) be impaired as a result of a disruption or interference, this should be reported to the nearest U.S. Coast Guard Captain of the Port, District Commander or Vessel Traffic Center as soon as possible; and, await further directions (per 33 CFR 164.53).
All operators should be aware, vigilant, and immediately report GPS disruptions to the U.S. Coast Guard Navigation Center (NAVCEN). The report will be disseminated to the U.S. Air Force GPS Operations Center and the Federal Aviation Administration in an attempt to identify the problem and correlate with any other GPS incidents in the same general geographic location. Depending on the severity of the report, NAVCEN may refer it to law enforcement and/or other federal agencies for further investigation.
Reporting a disruption — or other navigation hazards or aids to navigation outages — is simple, and can be done electronically (http://www.navcen.uscg.gov, the preferred method) or via phone call to the NAVCEN (703- 313-5900), 24 hours a day.
In his final State of the Union address, delivered Jan. 12, President Obama signaled his intent to invest in a 21st century transportation system.
U.S. Transportation Secretary Anthony Foxx has revealed part of the president’s proposal: a 10-year, nearly $4 billion investment to accelerate the development and adoption of safe vehicle automation through real-world pilot projects.
Secretary Foxx also announced that the U.S. Department of Transportation (DoT) is removing potential roadblocks to the integration of innovative, transformational automotive technology that can significantly improve safety, mobility and sustainability.
Secretary Foxx made the announcement at the North American International Auto Show in Detroit, where he was joined by leaders in technology, executives of traditional auto manufacturers, and newcomers to the industry.
“We are on the cusp of a new era in automotive technology with enormous potential to save lives, reduce greenhouse gas emissions, and transform mobility for the American people,” said Secretary Foxx. “Today’s actions and those we will pursue in the coming months will provide the foundation and the path forward for manufacturers, state officials, and consumers to use new technologies and achieve their full safety potential.”
The president’s FY17 budget proposal would provide nearly $4 billion over 10 years for pilot programs to test connected vehicle systems in designated corridors throughout the country, and work with industry leaders to ensure a common multistate framework for connected and autonomous vehicles.
Secretary Foxx also unveiled policy guidance that updates the National Highway Traffic Safety Administration’s (NHTSA) 2013 preliminary policy statement on autonomous vehicles. The new guidance, just released, reflects the reality that the widespread deployment of fully autonomous vehicles is now feasible.
“NHTSA is using all of its available tools to accelerate the deployment of technologies that can eliminate 94 percent of fatal crashes involving human error,” said NHTSA Administrator Mark Rosekind. “We will work with state partners toward creating a consistent national policy on these innovations, provide options now and into the future for manufacturers seeking to deploy autonomous vehicles, and keep our safety mission paramount at every stage.”
DOT is committing to the following milestones in 2016:
Within six months, NHTSA will work with industry and other stakeholders to develop guidance on the safe deployment and operation of autonomous vehicles, providing a common understanding of the performance characteristics necessary for fully autonomous vehicles and the testing and analysis methods needed to assess them.
Within six months, NHTSA will work with state partners, the American Association of Motor Vehicle Administrators, and other stakeholders to develop a model state policy on automated vehicles that offers a path to consistent national policy.
Secretary Foxx encouraged manufacturers to submit rule interpretation requests where appropriate to help enable technology innovation. For example, NHTSA responded to an interpretation request from BMW confirming that the company’s remote self-parking system meets federal safety standards. Click here to read this interpretation.
When interpretation authority is not sufficient, Secretary Foxx further encouraged manufacturers to submit requests for use of the agency’s exemption authority to allow the deployment of fully autonomous vehicles. Exemption authority allows NHTSA to enable the deployment of up to 2,500 vehicles for up to two years if the agency determines that an exemption would ease development of new safety features.
DOT and NHTSA will develop the new tools necessary for this new era of vehicle safety and mobility, and will consider seeking new authorities when they are necessary to ensure that fully autonomous vehicles, including those designed without a human driver in mind, are deployable in large numbers when they are demonstrated to provide an equivalent or higher level of safety than is now available.
In 2015, Secretary Foxx refocused the national dialogue about the future needs of our transportation infrastructure by releasing Beyond Traffic, a report examining the challenges facing America’s infrastructure over the next three decades. This draft framework has already influenced decisions by elected officials, planners and stakeholders nationwide, the DOT said.
In December 2015, the Secretary launched the Smart City Challenge, a national competition to implement bold, data-driven ideas that make transportation safer, easier and more reliable. He also worked to accelerate the DOT’s efforts to incorporate vehicle-to-vehicle (V2V) communication technology into new vehicles.
Real-time single-frequency precise point positioning for cars and trains
By Peter de Bakker and Christian Tiberius
INNOVATION INSIGHTS with Richard Langley
“IT’S GETTING BETTER ALL THE TIME.”This refrain from the Beatle’s song could well describe precise point positioning or PPP. PPP is a positioning technique that relies on GNSS carrier-phase measurements (in addition to code or pseudorange measurements) from a user’s receiver along with satellite orbit and clock data much more precise (and accurate) than that included in broadcast satellite navigation messages to achieve accuracies down to the centimeter level. It also requires a more sophisticated model of the measurements compared to that used in most consumer GNSS equipment and even some professional devices, including accounting for residual tropospheric propagation delay, carrier-phase windup, and even solid Earth tides.
PPP has been around for more than a decade and ongoing research has gradually improved its capabilities. Until recently, it has been used primarily with dual-frequency GPS observables. However, the technique is not restricted to GPS. It works equally well with observables from other constellations including GLONASS, Galileo and BeiDou. As long as precise orbit and clock products are available (typically from the International GNSS Service or its participating analysis centers), then PPP positioning solutions are possible. And, single-frequency PPP is also possible. The primary advantage of dual-frequency PPP is that the ionospheric propagation delay is almost completely removed by linearly combining the measurements on the two frequencies, taking advantage of the dispersive nature of signal propagation through the ionosphere. But, if good predictions of the ionospheric delay at, say, the L1 GPS frequency are available, then it is possible to do single-frequency PPP. While not as accurate as dual-frequency PPP, the technique is considerably more accurate than typical pseudorange point positioning (the so-called Standard Positioning Service).
PPP is also traditionally a post-processing technique. That is, data is collected but it is not processed until some later convenient time when the necessary precise products are available. Such an approach is useful for many applications but clearly not for navigation, which requires real-time positioning. But in the past few years, a number of commercial and non-commercial entities have started streaming real-time satellite orbit and clock corrections over the Internet and various radio links, making real-time PPP a reality.
In this month’s Innovation column, we bring together, perhaps for the first time, single-frequency and real-time PPP. Our authors describe a series of experiments they have conducted on roadways and a railway achieving sub-meter horizontal positioning at a 95 percent confidence interval. Such accuracies may already be sufficient for freeway lane and railway track guidance. But we might expect even better accuracies in the future. After all, PPP is getting better all the time.
The single-frequency precise point positioning (SF-PPP) method, developed at Delft University of Technology, was previously demonstrated to provide lane-level position accuracy on a freeway in post-processing mode. Important applications of SF-PPP are lane-level traffic state estimation and lane-level specific driver advice for next-generation car navigation. For a functional system, as well as for advanced experiments in this field, the computed positions have to be available in real time. Therefore, a new real-time implementation of the SF-PPP method was developed as part of the Dutch Dynamic Lane Guidance project. In this article, we outline aspects of the real-time implementation, and we present experimental results from this new implementation collected on a busy freeway in the Netherlands and in a parking lot, as well as results from a railway experiment.
In these experiments, a test vehicle was equipped with a low-end, automotive-type single-frequency receiver with a patch antenna to collect raw GPS observations. A 3G mobile communications link was used to obtain data-correction streams over the Internet using the Ntrip protocol. The SF-PPP processing was performed on a laptop computer onboard the vehicle, in real time. Various forms of ground-truth positions were used to assess the real-time SF-PPP positioning accuracy. For some of our tests, the vehicle was also equipped with high-end GPS antennas and receivers to provide ground truth. The position solutions obtained with the SF-PPP algorithm have been compared to (post-processed) network-RTK solutions using the Netherlands Positioning Service (NETPOS). Additional validation was performed by means of a 5-centimeter-accuracy road-infrastructure map from Rijkswaterstaat, the Dutch Ministry of Infrastructure and the Environment, and by a centimeter-level a priori ground survey.
The new real-time SF-PPP software was tested successfully with performance comparable to our previous post-processing software, and meeting the required accuracy for freeway lane identification. Statistics on the performance are provided, as well as their dependence on a number of external parameters including the number of available satellites.
Precise corrections from both the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR) and the International GNSS Service (IGS) were used. Delays in the correction streams vary between providers and can increase further in the event of a time-out of the mobile link. The influence of these delays is considered, and an optimal approach for dealing with outages is discussed.
PPP Model and Corrections
The GNSS positioning model is non-linear. The observations are non-linear functions of the unknown parameters plus noise.
To solve for the unknown parameters (including the receiver position coordinates), through least squares estimation, the model must be linearized around an approximate solution.
In our SF-PPP model, the primary observations are, from each satellite, the pseudorange measurement and the carrier-phase measurement. The unknown parameters are the receiver position vector and the receiver clock offset, both of which are involved in the linearization, and also the ambiguity, associated with the carrier-phase measurement, for which the model is already linear.
In the context of PPP, it is important to note that in addition to the linearization around the initial approximate values, the computed observations contain a number of a priori model values for parameters which are not estimated, including:
The precise satellite position and clock offset (including the relativistic effect): The GPS satellite positions and clock offsets are computed from the broadcast products (navigation message) and corrected with real-time data streams via Ntrip. The correction streams of DLR and IGS were used at different times as detailed in Table 1. In post-processing older files, the satellite orbits and clocks are taken from sp3 files, but to keep the processing as close as possible to the real-time functionality, these are first converted to corrections to the broadcast products.
The (neutral) troposphere delay: The troposphere delay is modeled with the a priori Saastamoinen model using the Ifadis mapping function and parameters from the 1976 U.S. Standard Atmosphere.
The ionosphere delay and satellite differential code bias: The ionosphere delay is computed a priori using the one-day predicted Global Ionosphere Maps (GIMs) from the Center for Orbit Determination in Europe (CODE), together with the corresponding differential code biases.
The carrier-phase observations are corrected for the phase wind-up at the receiver and satellite. The user orientation is estimated from the vehicle velocity vector.
TABLE 1. Four SF-PPP field tests.
Besides the primary observations, the ambiguity estimate from the previous epoch can be added to the current epoch as an additional observation per satellite, because it is assumed to be constant in the absence of a cycle slip.
Observations from different epochs are assumed to be uncorrelated, and consequently the ambiguity estimates from previous epochs are uncorrelated to the current observations. Observations to different satellites are also assumed to be uncorrelated.
The carrier-phase ambiguities are the only parameters propagated from a previous epoch to the current epoch. The receiver position coordinates (and receiver clock offset) are estimated each epoch anew — no vehicle dynamics model is involved.
The computed positions are finally corrected for solid Earth tides with an efficient numerical model. Computed positions result in the International Terrestrial Reference Frame (ITRF) 2008 at the epoch of the observations.
In parallel with the positioning filter, statistical hypothesis testing is used to detect errors in the observations or propagated ambiguities (such as those caused by excessive multipath or a cycle slip), based on the detection, identification and adaptation (DIA) procedure. First, an overall model test is run at each epoch to test the validity of the model and observations. If the test is rejected, data snooping is applied to determine which observation is most likely to have caused the problem. If one of the pseudorange measurements is identified, it is removed from the model. If either a carrier-phase measurement or ambiguity is identified, the ambiguity for that satellite is reset; that is, the propagated ambiguity is removed.
Experiments
Four field tests that we have carried out are considered here.
In October 2012, more than 100 laps were driven over a 5-kilometer stretch of the A13 freeway between Delft and Rotterdam. The data collected were reprocessed to validate the new real-time software implementation (but obviously carried out in post-processing mode).
The first real-time tests were performed in December 2014 and later in May 2015 on the same stretch of the A13 freeway.
In May 2015, a third dataset was collected on a recently constructed and nicely outlined parking lot in Delft.
In July 2015, a train carriage was equipped with a GPS receiver and data were collected on a train trip from the center of The Netherlands to the far southern part — a distance of more than 200 kilometers.
Details of the four field tests are collected in Table 1.
Ground Truth
In our earlier experiments, the ground truth for the vehicle positions was computed with measurements from high-end equipment onboard the same vehicle. Both the antenna of the SF-PPP receiver and the high-end antennas were rigidly connected to a wooden beam on the roof rack of the van (positions of the two high-end antennas at both ends of the beam were obtained through network RTK GPS). As our results from this experiment show, the performance, and especially the precision, is very good, but a moderate bias of 17 centimeters in the cross-track direction was observed (see FIGURE 1 and TABLE 2). The suspect cause of this bias was the antenna location, close to the side of the vehicle and not attached to the metal roof itself.
FIGURE 1. 2D histogram of SF-PPP position errors (with respect to the network RTK GPS solution) in horizontal directions for the 2012 test on the A13 freeway, expressed in local east and north directions (left), and in cross-track and along-track directions (right). The color indicates the number of samples in each bin.TABLE 2. Statistics of the position errors in each direction, for the 100 laps on the A13 freeway.
Therefore, during more recent experiments, the test vehicle was only equipped with a patch antenna for the low-end, automotive-type GPS receiver, and attached directly to the roof of the car, in the middle of the centerline of the vehicle. In this case, the metal roof acts as a ground plane for the antenna, improving the gain and not acting as a source of multipath. However, this setup also has complications for the accuracy assessment. Thus, instead of computing accurate ground truth from the measurements from high-end equipment directly near the test receiver, a number of other ways were used to determine the ground truth.
During the first real-time test on the A13 freeway, a 5-centimeter accurate road infrastructure map from Rijkswaterstaat was used as previously mentioned. This comparison was done both visually and numerically.
For our next experiment, we selected a recently constructed parking lot with a simple, neat rectangular layout. By surveying the corners of the rectangle and using the repetitive pattern, a schematic drawing of the parking lot was made, and used to evaluate the positioning performance in a visual manner. The car was first driven over the lined-up parking spaces in a lengthwise manner, circling round at each end of the parking lot, and changing lanes once each lap at the same point. Then the car was driven along the edges of the rows of parking spaces to and fro over the parking lot.
SF-PPP positions were obtained live in the vehicle while driving. The raw (single-frequency) observations of this experiment were also post-processed with the RTKLib software package using the nearby permanent DLF1 station at the TU Delft GNSS observatory on a very short baseline (less than 1 kilometer). The ambiguity-fixed results could then be used to also numerically assess the SF-PPP positioning performance.
For the test on the train, again the network RTK GPS solution provided the ground truth positions. Two antennas were mounted along the centerline of the carriage at a fixed offset from each other: a patch antenna for the single-frequencyreceiver and a geodetic antenna for the ground truth. With this known offset, and the direction of motion, the ground truth position for the single-frequency receiver was obtained.
The ground-truth positions, either in the European Terrestrial Reference System (ETRS) 89 (from NETPOS or our own survey) or in the local national reference frame Rijksdriehoeksmeting (National Triangulation System) / Normaal Amsterdams Peil (Amsterdam Ordnance Datum) or RD/NAP, have been transformed into ITRF2008, to allow for comparison with the SF-PPP positions.
Computational Performance, Data Rates
The real-time software was used under the 64-bit Windows 8.1 operating system on a moderately fast laptop with i5-4200U CPU running at 1.60 GHz. The software consists of uncompiled Matlab R2014b scripts and functions using timer objects to repeatedly read in new observations, corrections and ephemerides, and to update the position computation. The software can run with data arriving at about 20 Hz in the current state on this platform, but was used with 5-Hz data because of limitations of the receiver to provide raw data and to prevent any overrun. It should be noted that only a few obvious potential computational bottlenecks were targeted; the software was not optimized for efficiency.
The RT SF-PPP implementation relies on a 3G mobile Internet connection for a number of data products. The ionosphere map, which is a predicted product (24 hours ahead), comes as a 200-kilobyte file (and 5 kilobytes for the associated differential code biases), which covers the globe and is valid for 24 hours. The file contains 13 maps at 2-hour intervals, between which interpolation in time is required.
Spatial interpolation is also required for the ionosphere pierce point of each satellite signal, between the grid points in the map (at intervals of 5 degrees in longitude and 2.5 degrees in latitude). The satellite orbit/position corrections (every 60 seconds) and satellite clock corrections (every 10 seconds) are retrieved over the Internet using the Ntrip protocol by means of the Bundesamt für Kartographie und Geodäsie (BKG) Ntrip Client (BNC), which passes these on to Matlab.
The data-rate used by this correction stream is about 1 kilobit per second. The corrections are applied to the broadcast ephemerides (in quasi-Keplerian-element form), which are therefore also required. These satellite ephemerides can be extracted by the GPS receiver itself (from the GPS navigation message), but in our implementation are also collected via Ntrip for convenience only, with a bandwidth consumption of 6 kilobits per second. Note that, much like the software implementation itself, the data stream has not been optimized for any particular bandwidth limitation. For instance, orbit and clock corrections are needed only for those satellites in view, and hence transmitting the data for all satellites of the constellation is not needed.
Results
In this section, we present the results of our tests, followed in the next section with a discussion of important common factors affecting accuracy and continuity of RT SF-PPP.
Road-Test A13 Freeway (100 Laps). Under different conditions, we collected a large amount of data with a van, driving repeatedly the same 5-kilometer stretch of road on the A13 freeway from Rotterdam to Delft. The test amounted to almost a full day of driving.
2D histograms of the results are shown in Figure 1 with corresponding statistics in TABLE 2. Note a small bias in the cross-track direction. The total number of position solutions was 2.0× 105.
Road-Test A13 Freeway (Real Time). The results of the real-time freeway road test are shown in FIGURE 2. The different lanes used by the vehicle are clearly visible in the figure. The number of GPS satellites is indicated by the color bar. Shown is the Delft-Zuid / TU Delft exit of the A13 freeway, roughly a 300 × 300 meter area, taken from the Digitaal Topografisch Bestand (DTB) of Rijkswaterstaat. Note that only the cross-track performance can be assessed in this manner, but fortunately this is exactly the performance aspect that is most interesting for the target application of lane identification. Note also that if the vehicle was not driving exactly in the middle of the lane, which to some extent is unavoidable, this effect cannot be separated from the positioning errors.
FIGURE 2. SF-PPP solution displayed on a 5-centimeter accurate road infrastructure map, on Dec. 18, 2014.
The 95-percent error southbound and northbound is 0.65 meters and 0.58 meters respectively, in the cross-track direction.
Road-Test Parking Lot.FIGURE 3 shows an aerial photograph (left) and schematic drawing (right) of the 3M company parking lot in Delft showing measured positions and driven tracks. The lines in red and yellow represent the measured tracks while driving the same loop over the parking lot again and again (more than 60 times in total), and the purple lines show the track while driving around and following the parking space boundaries with the left front wheel of the test vehicle (4 laps). These lines show both the SF-PPP position error and the driver error. The white parking spaces are each 2 meters wide.
FIGURE 3. Aerial photograph, from Google Earth, (left) and schematic drawing (right) of the parking lot in Delft showing measured positions and driven tracks.
The position errors in local north, east and up directions for part of the first dynamic session, of about 4.5 laps, of the 3M parking lot experiment (lane change 1) are shown in the upper panel of FIGURE 4. We see a clear periodic signal as well as a bias in each direction. The driving direction gives an approximation of the heading (shown in the bottom panel), which confirms that the periodic signal coincides with the driven laps.
FIGURE 4. Position errors (top) in local north, east and up directions and heading (bottom) for part of the first dynamic session, about 4.5 laps, of the 3M parking lot experiment (lane change 1).
The figure shows that the errors in the position solution are on the order of 0.2 meters, and consist of a bias in each of the three directions and a periodic signal with a period equal to the lap-time (confirmed by the driving direction of the vehicle). Since the bias does not depend on the orientation of the vehicle, and given the slow variation over time, the most likely cause is a residual ionosphere error or errors in the satellite products. The repeating pattern, on the other hand, is most probably related to multipath or near-field effects related to the vehicle antenna.
Rail-Test Amersfoort to Simpelveld. The train carriage with the GPS antennas installed was pulled by a 1955-built diesel-electric locomotive. A trip of more than 200 kilometers was made, over the main Intercity Network of Nederlandse Spoorwegen (NS) / ProRail (Dutch Railways). Only the last 20 kilometers were on a local line to a historic railway station.
The overhead power line (about 1 meter above the GPS antennas) and portals seem to have no impact on the SF-PPP positioning performance. An example of the positioning accuracy is shown in FIGURE 5. The figure shows position error scatter for an almost 20-kilometer stretch of nearly straight east-west track through rural and forest areas (Weert to Roermond). The time span of the data is 10 minutes, and the data rate was 5 Hz. SF-PPP positions were compared with NETPOS network RTK GPS solutions. Generally, eight satellites were received and used in the SF-PPP solution. The corresponding error statistics are presented in TABLE 3.
FIGURE 5. Position error scatter for an almost 20-kilometer stretch of nearly straight east-west track through rural and forest areas (Weert to Roermond); 10 minutes of data at 5 Hz.TABLE 3. Statistics of the position errors, over 2994 epochs, in along- and cross-track directions, for the position scatter shown in Figure 5.
A heavy steel-construction bridge along the route at the River Lek near Culemborg, 15 kilometers south of Utrecht, was found to degrade positioning performance considerably. The heavy steel construction of the bridge hampers reception of GPS satellite signals. The positioning performance on the bridge is shown in FIGURE 6. The computed SF-PPP trajectory overlaid on a Google Earth aerial photograph is shown on the left.
FIGURE 6. Positioning performance on the Lek Bridge. Left: measured trajectory overlaid on a Google Earth aerial photograph. The number of satellites available is indicated by the color bar. Right top: SF-PPP positions in local east-north directions. Right bottom: Absolute cross-track offset of position solution with respect to a straight line, as a function of time.
From the positions, one can clearly see the train driving straight on the right-hand track (going south) on the ramp onto the bridge, and on the ramp down from the bridge. However, on the bridge itself, position solutions show considerably larger variations of up to 8 meters. The image shows a 250-meter stretch of the track. Also, the number of satellites available, and used in the position solution, drops considerably (indicated by the color bar) while the train is on the bridge. On the right of the figure at the top, the SF-PPP positions in local east-north coordinates are shown along with a straight line between the first and last epochs, representing the assumed straight track. The plot at bottom right shows the absolute cross-track offset of the position solutions with respect to the straight line, as a function of time, over 250 5-Hz epochs.
Analysis
Two factors significantly affect the performance of our tests: the number of satellites available and the continuity and latency of the corrections.
Number of Satellites.As can be expected, the SF-PPP position accuracy depends to a large extent on the number of satellites used to compute the solution. For the third test, the road-test in the 3M parking lot, the three-dimensional position error (SF-PPP versus RTK GPS) is shown as a boxplot in FIGURE 7 in which various accuracy measures are plotted as a function of the number of satellites for the second and longest dynamic part of the test (lane change 2), consisting of about 12,000 epochs of data. During this session, the available number of satellites varied between 10 and 12. This number was reduced artificially by increasing the elevation mask angle to 15 and to 30 degrees. The red lines show the medians, the boxes show the 25th and 75th percentiles, the dashed lines cover all data points not considered outliers, and outliers are plotted with red plus signs. The graph shows a clear improvement going from six to seven or more satellites.
FIGURE 7. Boxplot of 3D position error vs. the number of satellites for the second and longest dynamic part of the 3M parking lot test (lane change 2).
PPP Correction-Stream Outages.To determine the optimal approach to an interruption in the correction data stream, we studied the variation of the corrections over time. Suppose we lose reception of the correction stream at epoch 0, and we keep using the last-received corrections (simply hold onto them). Then the change in values can be interpreted as the additional error introduced in the positioning algorithm by the outage on the mobile link. The effect is not catastrophic. Only after about 200 seconds do the additional satellite clock errors grow to the decimeter level. The position errors remain even smaller.
However, one might wonder whether this can be improved further by performing a linear extrapolation of the corrections, for example, using a number of previous epochs. We looked at what would happen in this case if 5 minutes of previous data are used. For the clock errors, there is no real benefit — the errors only grow larger. But the position errors do remain smaller during the first 5 minutes of extrapolation. After that time, the errors are larger than those without the linear extrapolation (just holding onto the last corrections). The effect of increasing the order of the polynomial extrapolation was also considered. The polynomials of different order outperform each other at different extrapolation times, and also the number of previous epochs used for the polynomial estimation impacts this. Further optimization to reduce the satellite position errors might well be possible, but may be of marginal value, since, the extrapolated clock error is dominant and polynomial extrapolation does not improve this. Simply using the most recent corrections is thus a straightforward and acceptable approach.
Conclusions
In this article, we outlined a real-time implementation of single-frequency GPS precise point positioning. With a fairly low-cost GPS receiver and reception of a modest correction data stream, it is possible to achieve sub-meter horizontal positioning accuracy, in real-time, live in the vehicle (95-percent error of better than 1 meter). Actual results were shown from four field tests: two tests using a vehicle on a freeway, a vehicle test in a parking lot, and one test on a train.
The number of satellites used in the position solution has a big effect on the positioning performance; seven or more satellites yields a good position accuracy. And up to 5 minutes outage of the satellite position and clock corrections does not seem to pose a serious threat to SF-PPP positioning performance.
Acknowledgments
The Dynamic Lane Guidance project under which the first road test was carried out was funded by the Ministry of Infrastructure and Environment, the Province of Noord-Brabant and the Eindhoven Regional Government in the context of Brabant in-car III. This project was carried out in close cooperation with colleagues in the Transport and Planning Department at TU Delft.
We acknowledge the provision of the Real-Time Clock Estimation (RETICLE) satellite clock products by André Hauschild at DLR for several of our field tests. We are also grateful for the use of the IGS Real-Time Service. Also, we acknowledge the provision of the NETPOS network RTK GPS service as ground truth by Lennard Huisman of Kadaster, the Dutch Land Registry and Mapping Agency. Colleague Hans van der Marel analyzed the NETPOS RTK-GPS solution of the train test. Colleagues of the TU Delft Railway Engineering Department offered the opportunity to carry out the test on the train trip from Amersfoort to Simpelveld.
Manufacturers
The vehicle receivers used for the tests were u-blox AG TIM LP and 7P modules in evaluation kits fed by a Tri-M Technologies Inc. Big Brother SM-66 or Taoglas Dominator AA.161 antenna. A Trimble Navigation R7 receiver with a Zephyr Geodetic antenna was used to establish ground truth for some tests.
PETER DE BAKKER is a researcher in the Faculty of Civil Engineering and Geosciences at Delft University of Technology (TU Delft). He recently finished his Ph.D. dissertation on user algorithms for GNSS precise point positioning, and is working on localization for automotive applications, including autonomous vehicles.
CHRISTIAN TIBERIUS is an associate professor in the Faculty of Civil Engineering and Geosciences at TU Delft. He has been involved in GNSS positioning and navigation research since 1991, currently with an emphasis on data quality control, satellite-based augmentation and precise point positioning.
Further Reading
• Earlier Work on Single-Frequency Precise Point Positioning
“Lane Identification with Real Time Single Frequency Precise Point Positioning: A Kinematic Trial” by R.J.P. Van Bree, P.J. Buist, C.C.J.M. Tiberius, B. van Arem and V.L. Knoop 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. 314–323.
“Real Time Satellite Clocks in Single Frequency Precise Point Positioning” by R.J.P. Van Bree, C.C.J.M. Tiberius and A. Hauschild in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Ga., Sept. 22–25, 2009, pp. 2400–2414.
“Single-frequency Precise Point Positioning with Optimal Filtering” by A.Q. Le and C. C. J. M. Tiberius in GPS Solutions, Vol. 11, No. 1, 2007, pp. 61–69, doi: 10.1007/s10291-006-0033-9.
• Single- vs. Dual-Frequency Precise Point Positioning
Improved Convergence for GNSS Precise Point Positioning by S. Banville, Ph.D. dissertation, Department of Geodesy and Geomatics Engineering, Technical Report No. 294, University of New Brunswick, Fredericton, New Brunswick, Canada. Recipient of The Institute of Navigation 2014 Bradford W. Parkinson Award.
“Variations of Box Plots” by R. McGill, J.W. Tukey and W.A. Larsen in The American Statistician, Vol. 32, No. 1, Feb. 1978, pp. 12–16, doi: 10.2307/2683468.
Magellan is showcasing its new eXplorist TRX7 off-road vehicle navigation solution for the 4×4 and Powersports vehicle consumer market at CES 2016, a consumer electronics and technology trade show held Jan. 6–9 in Las Vegas. The OHV navigation solution delivers detailed 3D maps, more than 44,000 vehicle trails and community generated trails, improved driver safety and a superior user experience, the company said in a news release.
The TRX7 will be displayed in the Magellan booth at CES, located in South Hall MP25441.
“Magellan’s new eXplorist TRX7 is the only complete off-road navigator for adventuring,” said Stig Pedersen, associate vice president of product management for Magellan. “Pre-loaded trail maps and crowd-sourced trails provide a constantly updating platform, allowing users to plan adventures, navigate, and add pictures and comments to trails. Finally off-roaders have an all-in-one solution that will safely guide them through some of the most fun and exciting trails in the U.S. and Canada.”
The device
Built to withstand the harsh demands of off-roading, the Magellan eXplorist TRX7 features:
16GB onboard memory and a 64GB MicroSD card expansion slot.
Three different mounting options: Windshield Suction Cup Mount, Genuine Ram Handlebar Rail Mount or Genuine Ram Windshield Suction Cup Mount.
The maps
The Magellan off-road vehicle platform’s trail maps are cloud-based, dynamic, and will continue to grow and be improved by both Magellan and through crowd-sourced additions from the Magellan OHV user community, the company said. The off-road maps feature high-resolution 3D and 2D terrain and contour elevation lines; food, gas, lodging and general service POIs; third party trail guides; and more.
The Magellan TRX7 allows users plan, track and save trail rides. Its OHV web portal lets users add pictures and comments to their trail rides and share them with friends, family, and off-road and outdoor communities. Members of Magellan’s off-road vehicle online community earn achievement badges for posting and sharing “dirt miles” traveled and total number of trails posted.
The portal also is integrated to social media sites such as Twitter, Facebook and Instagram, users are able to post their greatest trail adventures.
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.
Broadcom Corporation has added a new GNSS wireless connectivity chip to its automotive portfolio, which it unveiled at CES 2016, being held this week in Las Vegas.
Automotive GPS shipments are expected to more than double by 2022, creating significant opportunities among component suppliers and increasing competition for market share. The chip offers wideband capture radio technology for simultaneous tri-band reception of all visible GNSS satellites including GPS, Galileo, QZSS, GLONASS, BeiDou and global SBAS augmentation systems.
Broadcom’s BCM89774 provides improved location and positioning while lowering power consumption for in-vehicle applications and reduces bill of materials cost for car makers, by integrating the sensor hub and CPU on a single chip.
The BCM89774 delivers original equipment manufacturers (OEMs) one of the most accurate solutions available today, Broadcom said. The new chip also improves positioning in dense urban environments and foliage-blocked areas to enhance the consumer experience.
Optimized to meet the rigorous standards of the automotive industry, the BCM89774 has been tested to AECQ100 automotive environmental stress requirements, is manufactured in TS16949 certified facilities, and offers full production part approval process (PPAP) support.
“Broadcom’s new GNSS connectivity chip for automotive keeps car makers and tier one suppliers ahead of the curve with advanced precision and reduced power consumption while lowering BOM cost,” said Richard Barrett, Broadcom Director of Automotive Wireless Connectivity. “By delivering premium products that meet automotive grade requirements, we are positioned for growth in this accelerating market.”
Key Features:
Low-power mode for emergency service and theft tracking applications
Location awareness capabilities added to traditional functions of a sensor hub for lower power consumption and BOM costs
Simultaneous reception of GPS, GLONASS, BDS, QZSS and Galileo navigation satellites
Support for global Satellite Based Augmentation System (SBAS) system
Management of CAN BUS inputs and sensors such as accelerometers, gyroscopes, and magnetometers to provide a fused sensor data tracking subsystem
Best-in-class acquisition, tracking sensitivity and time-to-first-fix in both cold and hot starts
Full pass through capability for external host-based systems
Tested to AECQ100 automotive environmental stress requirements and manufactured in TS16949 certified facilities
Full production part approval process (PPAP) support
Qualcomm Technologies has introduced its latest Qualcomm Snapdragon automotive processors, the Snapdragon 820 Automotive family, offering a scalable next-generation infotainment, graphics and multimedia platform with machine intelligence and a version with integrated LTE (long-term evolution)-Advanced connectivity.
The Snapdragon 820A is Qualcomm Technologies’ newest automotive-grade system-on-chip (SoC). Qualcomm Technologies has taken a modular approach to designing the Snapdragon 820A, enabling a vehicle’s infotainment system to be upgradable through both hardware and software updates, thereby enabling vehicles to be easily upgraded with the latest technology.
The Snapdragon 820A’s sensor integration provides cognitive awareness and vehicle self-diagnostics, supports ADAS features for improved vehicle safety systems, and provides location and navigation through GNSS and dead-reckoning technologies.
Qualcomm Technologies is demonstrating the upgradeable module at the Qualcomm Automotive booth, North Hall #915, at CES 2016, being held in Las Vegas this week.
The Snapdragon 820A family is based on 14-nm FinFET advanced process node running Qualcomm Technologies’ custom 64-bit Qualcomm Kryo CPU, Qualcomm Adreno 530 GPU, Qualcomm Hexagon 680 DSP with Hexagon Vector eXtension (HVX), Qualcomm Zeroth machine intelligence platform, and the Snapdragon 820Am version with integrated X12 LTE modem capable of 600 Mbps downlink/150 Mbps uplink. The 820A is engineered with custom-built, highly optimized cores designed for heterogeneous computing — the ability to combine its diverse processing engines within the SoC, such as the CPU, GPU and DSP cores, to achieve previously unattainable performance and power savings.
The Zeroth initiative, a machine intelligence platform on Snapdragon 820A, is designed to enable automakers to develop state-of-the-art deep learning-based solutions using neural networks for advanced driver assistance systems (ADAS) and in-vehicle infotainment scenarios, and run them efficiently on embedded platforms in the vehicle. Zeroth accelerates execution of deep neural networks using the heterogeneous compute engines that are part of the Snapdragon 820A. A Zeroth-powered development kit for automotive solutions will be available for the Snapdragon 820A.
“With the Snapdragon 820 Automotive processing platform, we are delivering an unprecedented level of performance and technology integration designed to significantly enhance the consumer’s safety and in-vehicle experience. Never before has the unparalleled combination of integrated LTE cloud connectivity, powerful heterogeneous computing, leading-edge multimedia performance and breakthrough machine learning capabilities been delivered in a single chip, fully integrated, automotive grade solution,” said Patrick Little, senior vice president and general manager, automotive, Qualcomm Technologies.
“The automotive industry has long been asking for a single scalable solution capable of delivering the rich user experience and level of performance, connectivity and upgradability that consumers are accustomed to on their personal mobile devices,” Little said, “including real-time cloud connectivity and navigation, immersive 4K graphics and video displays, the flexibility of hardware and software upgradability, and the deep learning and remote diagnostic capabilities needed to deliver the next level of safety performance in the vehicle. The Snapdragon 820 Automotive platform has been designed to deliver all of these capabilities and much more.”
The version with integrated X12 LTE modem is designed to support continuous in-car and cellular connectivity, featuring the leading 4G LTE Advanced Pro that can support up to 600 Mbps download/150Mbps upload speeds, stream HD movies into the car, serve as a Wi-Fi hotspot supporting 802.11ac 2×2 MIMO, connect multiple mobile devices inside the car, and support 802.11p DSRC for V2X (vehicle to vehicle/infrastructure/pedestrian) communications. Local connectivity inside the car via Bluetooth supports content sharing between mobile devices brought into the car and the car’s infotainment system.
Qualcomm Technologies is also helping to lead the 3GPP in developing specifications for automotive V2X, for both LTE release 14 (LTE V2X) and 5G standards.
“Like Qualcomm Technologies, AT&T is committed to the connected car and takes a similar approach to technology development with the AT&T Drive platform, offering a global, modular solution to automakers to enable best-in-class user experiences for their drivers,” said Chris Penrose, senior vice president, Internet of Things, AT&T Mobility. “We design our solutions to provide better connectivity, flexibility and upgradability on our network, and Qualcomm Technologies’ development of the Snapdragon 820A Smart LTE Module is a prime example of this same approach to technology.”
By integrating advanced camera and sensor processing, the 820A supports critical always-on warnings and emergency services, extends standard cameras to Intelligent Cameras, and supports parking assist periphery vision features using surround view cameras. These features are supported by the on-chip Hexagon 680 DSP with HVX, which supports multiple automotive camera sensors connected simultaneously.
The Snapdragon 820A family of automotive-grade processors is designed for the automotive ecosystem and offers these features:
Scalable and modular platform offering pin, package and software-compatibility, with optional integrated LTE capability that is hardware and software upgradeable as wireless network technology evolves.
Supports vertical tiering options by offering the Snapdragon 820A family across premium to standard performance configurations.
Comprehensive software support for QNX, Linux and Android, as well as substantial platform-level integration of high value sub-systems to respond to the acceleration in refresh cycles while managing cost.
The connectivity, multimedia and graphics capabilities allow many real-time cloud based features, including streaming multimedia, enterprise collaboration, real-time maps and location services, remote diagnostics and one-touch telematics, with substantial potential for performance, connectivity and multimedia innovation for auto OEMs.
The upgradability option allows a wireless operator to offer an 820A Smart LTE Module concept for the version with an integrated modem that allows cellular connectivity to be updated through both hardware and software when new features become available on the cellular network.
Qualcomm Technologies is also collaborating with Aisin AW to develop the modular infotainment solution utilizing the Snapdragon 820A. “We expect the 820’s powerful features will deliver superior processing power, graphics performance and low power consumption demanded by next generation infotainment systems,” said Kyomi Morimoto, managing officer, Aisin AW.
Automotive samples of the 820A family are expected to be available in the first quarter of 2016. A number of concept vehicles and demonstrations based on the Snapdragon 820A, from Qualcomm Technologies and other automotive industry leaders, will be shown in the Qualcomm Automotive booth, North Hall #915 at CES 2016.
Automotive and consumer technology teams in Detroit and Silicon Valley remain hard at work preparing to kick-off the New Year with new technology at two of the nation’s biggest showcases of automotive connectivity: CES 2016, held Jan. 6–9 in Las Vegas, and the North American International Auto Show, held Jan. 11–24 in Detroit.
Samit Ghosh, Ph.D., president and CEO of P3 North America, has worked with U.S. automakers on connected vehicle technology since 2005. He shared his thoughts on the future of driving and what to expect at the upcoming shows in a news release from the company.
“Autonomous driving, information and entertainment systems will continue to take center stage in 2016 as automakers focus on chips, sensors and smartphone applications as key consumer differentiators,” Ghosh said. “In-car entertainment and safety capabilities provided through telematics and infotainment technologies are rapidly becoming the reasons consumers buy vehicles, so the stakes have never been higher.”
Underscoring the growing intersection of consumer technology and the car, Ghosh pointed to CES reports that its automotive exhibit space will grow 25 percent at the 2016 show, with nine auto makers and 115 automotive tech companies debuting products.
“Complex technologies require efficient processes,” Ghosh said. “The connected car ecosystem is complicated and faces many challenges, but automakers are beginning to think differently about the way they incorporate technology into cars. They need to start by rethinking their organizations and processes, breaking down organizational silos and taking an end-to-end view of all the touch points that spell success in the rapidly changing IoT ecosystem.
“Hot topics at this year’s auto shows will be the security of connected vehicle systems and the safety implications of evolving driver interfaces. Automakers also face the tough decision to remain proprietary or join the open source software movement, as smartphones become universal devices for controlling every consumer’s world. From personalized in-car entertainment to smart home integration, the car is becoming a critical link in our interconnected world,” he said.
According to Ghosh, in the software-focused world, carmakers can achieve far greater economies of scale by sharing technology with all other automakers. He cited GENIVI open source In-Vehicle Infotainment software as one force working to shorten development cycles and reduce OEM costs.
“As an independent systems integrator, P3 efficiently connects and unites large industry players to quickly and successfully innovate,” Ghosh said. “The way we manage projects and optimize our clients processes is extremely unique. Our international experience in both the automotive and the telecommunications industries gives us the exact perspective needed to help these converging industries accelerate the development of connected car technology.”
QantasLink, Australia’s largest regional airline, has selected the FreeFlight 1203C SBAS/GNSS sensor for retrofit into its DHC-8-200/300 series of aircraft.
