Category: Transportation

Can GNSS Drive V2X?
By Chaminda Basnayake, Tom Williams, Paul Alves, and Gérard Lachapelle

Communication-enabled vehicle safety has the potential to change transportation’s future, particularly vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), collectively represented as V2X. An automakers’ consortium conducted extensive field trials to determine GNSS service availability and accuracy for the V2X challenge.

V2X can include applications based on communications between any two or more entities on the road. Of all the potential V2X applications, V2V applications probably lead the way in terms of maturity of prototype development and test efforts. General Motors (GM) demonstrated the first working prototype V2V system in 2005. Information on further industry collaborative efforts in V2V system developments can be found at the U.S. Department of Transportation’s (DOT’s) IntelliDrive website. While a multitude of applications could be developed based on V2I capability, most of the related system prototype development efforts have taken place under the DOT’s Cooperative Intersection Collision Avoidance (CICAS) program.

Driving environments encountered in testing. Clockwise from top left: deep urban, urban thruway, local roads, mountains.

Accuracy Requirements

In terms of positioning accuracy requirements, Vehicle Safety Communications-Applications (VSC-A) prototype system capabilities as well as all V2X applications can be classified as:

Which Road. In this case, accuracy is only required to the extent of identifying the road traveled. For instance, if a vehicle is in a service road parallel to a freeway, knowing that it is on the service road and not on the freeway is sufficient. The need of a typical vehicle navigation device is another good example of this requirement category. The typical accuracy requirement for this case is better than 5 meters. However, this could be a relative accuracy requirement for certain applications. For instance, in a V2V scenario, one vehicle may only need to know if the other is on the same road or not, while in the absolute sense both vehicles could be in error by more than 5 meters. For V2I applications, however, this becomes an absolute accuracy requirement, as the infrastructure is always mapped and identified with respect to a global coordinate frame.

Which Lane. This accuracy level enables applications to identify other entities with lane level resolution. The typical requirement is 1.5 meters or better, which approximately corresponds to half of a lane width. A blind-spot advisor is a good example that requires this accuracy.

Where-in-Lane. This accuracy level enables the relative positioning of entities to better than 1 meter. Further refinements of blind-spot advisor-like applications are examples.

Availability Requirements

GNSS as a line-of-sight technology has obvious limitations in certain environments, and these limitations are well understood by the GNSS community. The focus of this study was to understand the limitations associated with a GNSS-only V2X solution such that requirements for augmentation technologies can be defined. Therefore, no availability requirements were set for the system; estimating availability of a GNSS-only solution was the goal.

Why So Complicated? At first glance, what needs to be done is straightforward; all V2X-capable entities need to be aware of each other’s positions. Hence, if all entities transmit their own location with respect to the same coordinate system, the problem is solved. Unfortunately, it’s not that simple.

Designing the system so that hundreds of entities, potentially using all sorts of GNSS software and hardware, can work together presents a significant challenge. This includes keeping backward compatibility way out into the future.

Even within the same receiver make and type, inclusion of a particular satellite in the solution of one vehicle can significantly affect the solution difference between vehicles. Inclusion of SBAS also contributes as a differentiator. In a V2X scenario, out of two adjacent vehicles, one vehicle may use SBAS while the other may not, due to hardware configuration or visibility. If none of the above situations occurred and everything else were ideal, transmitting just the current horizontal position of a V2X entity over-the-air (OTA) would be sufficient to do everything needed.

V2X thus requires a positioning system architecture that minimizes the impact of these complications and many other potential compatibility issues. Major system design considerations include:

Performance Requirements. The system must provide relative positioning accuracy that fits Which Road, Which Lane, or Where-in-Lane category and should identify the solution quality. For instance, a vehicle on a freeway with relatively open sky view may function in the Which Lane mode and may transition to Which Road mode as it enters an urban area with sky visibility limitations.

Deployment Constraints. The system must be affordable for automotive applications. This may also include considerations such as antenna placement, processing resource requirements, and power requirements.

Bandwidth Constraints. The volume of data transmission constitutes a major consideration for OTA communications. While some methods manage communication range and frequency as a way of optimally using the communication channels, keeping the OTA data volume to a minimum by design was a goal.

Study Goals

This study investigated the performance of two relative positioning methods: DPOS, a method of using the difference in position reported by two entities to calculate the 3D separation between the points; and real-time kinematic (RTK). While there are many other possible relative positioning methods, these two were selected as they collectively represent the most desirable availability and accuracy performance. In DPOS, vehicle coordinates are transmitted between vehicles in order for position differences between vehicles to be derived at each vehicle. In RTK, raw code and carrier-phase data is transmitted between vehicles, and the inter-vehicle position differences are calculated using RTK software in either fixed or float carrier-phase ambiguity mode at each vehicle. The RTK method is more intensive both from a data transmission and computational aspect, but retains only common satellites in the solution, eliminating the problem described earlier. Its use of carrier-phase measurements also makes it more accurate.

The study included two GPS receiver types. The first, a single-frequency L1 automotive-grade receiver, is identified as Type B receiver in this study. The second, identified as Type A, was of a higher quality with proprietary multipath mitigation technologies. Both receivers were capable of using WAAS support. Receiver B also allowed the user to reject selected satellites from its solution. These two devices were selected as they were capable of supporting both processing methods, and represent on the one hand an existing automotive-grade receiver, and on the other hand one that is expected to be a good representation of a product with technologies available for automotive deployment a few years from now.

Specific study goals were:
- Accuracy performance of DPOS and RTK methods when all vehicles use same GPS receiver type.
- Same when a receiver type or a receiver configuration mix is used.
- Dependency of the accuracy performance on the driving environment.
- Solution availability with same receiver and mix receiver combinations.
- Implications of non-continuous V2I coverage.
Prototype System

The system prototype (Figure 1) used for the study was a replica of the prototype relative positioning system implemented in the VSC-A project. It consists of a dedicated short-range communicatin (DSRC) interface with a DSRC radio, a GPS receiver/relative positioning module, and a sensor data handler.

Figure 1. VSC-A prototype relative positioning system.

In operation, a vehicle generates its own location information and GPS raw data in RTCM format and shares this data with other vehicles. OTA messaging was done using the SAE J2735 messages set with GPS raw data in RTCM format attached as optional data. As shown in Figure 1, RTCM v3 1002 messages were used to exchange VSC-A data. The system was also capable of using RTCM v3 messages 1001 & 1005 for V2I operation. The DPOS relative positioning logic was implemented in the sensor data handler, while the RTK implementation was done in a separate relative positioning module. This module takes in local and remote 1002 messages and outputs RTK data to the sensor data handler. Applications could access both RTK and DPOS relative positioning information from the sensor data handler.

Vehicle Setup. Two vehicles were used for the V2X data collection. Four different GPS L1-only test receiver types were installed on each vehicle:
- AW: high-quality receiver using WAAS corrections.
- BW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections enabled.
- BNW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections disabled.
- B24W: high-sensitivity automotive-grade receiver using a maximum of the four primary satellites in each of the six planes (minimum guaranteed constellation) and with WAAS ranging and corrections enabled.
As shown in Figure 2, the AW and B type receivers were connected to different GNSS antennas. These antennas were mounted on roof-racks attached to the vehicles (see Photo). The patch antenna for the Type B receivers was mounted on an aluminum-topped wooden pedestal to bring it to approximately the same height as that used by the AW receivers, to provide a ground plane and to prevent shading from other equipment on the roof-racks. The spacing between the antennas was accounted for in all analysis.

Figure 2. High-level V2V hardware setup on each of the two test vehicles.

Figure 2 also shows that only three of the four test receivers, AW, BW, and BNW, were connected to the computer that ran the RTK software. This computer calculated the inter-vehicle vector (IVV) using information exchanged over the DSRC radio link in real time. The vehicles each had a designated base relative to which the IVV was calculated; for Vehicle 1 it was BW and for Vehicle 2 it was AW. Thus the computer on each vehicle calculated three instances of the IVV, for example, the computer on Vehicle 1 calculated BW1–BW2, BW1–BNW2, and BW1–AW2 (where Ri denotes the receiver of type R on vehicle i).

Transmission and reception of data between the two vehicles required for the IVV RTK calculations were achieved using wave radio modules with two magnetically mounted 802.11p antennas on each vehicle for redundancy. During testing, Vehicle 1 generally followed Vehicle 2. To minimize potential interference of roof-mounted instruments on between-vehicle communications, the antennas on Vehicle 1 were located close to the front of the roof, while those on Vehicle 2 were located close to the rear of the roof. In each case, 15 centimeters of roof space were left to provide ground planes for the antennas.

We used the single-point navigation solutions logged from each test receiver to calculate the IVV for each receiver combination using the DPOS method in post-processing. No real-time data transfer between the vehicles was used for this method.

Reference values of the IVV were calculated in post-processing using both geodetic grade GPS/GLONASS L1/L2 receivers and GPS/INS integrated systems in differential mode. Both were connected to the antenna used by the AW receiver. Differential GPS calculations were enabled by using stationary receivers with antennas at precisely known WGS84 locations on top of a building at the University of Calgary.

Two study vehicles with antennas attached to the roof-racks.

Test Scenarios

V2V data was collected in and around the city of Calgary in August 2009. In the majority of the tests, Vehicle 1 followed Vehicle 2 with a separation of less than 300 meters, the stated effective range of the DSRC link. For most tests the inter-vehicle separation was between 30 and 150 meters. Some driving environments forced modifications of the default behavior; for example, on highways, vehicles moved in between the two test vehicles, necessitating lane changes. Approximately 52 hours of data was collected over 12 days. After rejecting data due to various faults such as reference-system malfunction, more than 45 hours of data remained.

Data was collected in the seven test environments listed in Table 1. These environments were selected in accordance with Federal Highway Administration descriptions. Each environment provided different challenges for GNSS-based positioning. Obviously the deep urban environment was challenging because the reduced number of visible satellites and the large amount of multipath meant that navigation solutions were both rare and of poor quality. As another example, the mountain environment was interesting because often almost half the sky was occluded by trees on the mountain side, leading to an asymmetrical visible GPS satellite constellation with the associated solution degradation. The photos at the beginning of this article show selected driving environments encountered during testing.

Table 1. Description of driving environments used in V2V tests.

V2V Solution Accuracy. Positioning accuracy of the individual receiver was first investigated to estimate the V2V relative positioning accuracy when using the DPOS method. This was done for the entire dataset.

Figure 3A shows a representative freeway dataset to illustrate overall trends: the absolute 2D mean position errors observed from all eight GPS receivers used in both vehicles. The first set of four receivers shown were the AW, BW, BNW, B24W receivers in the first vehicle (V1), and the second set of receivers were the same type in the second vehicle (V2). As a general trend, Type A receivers provided better absolute accuracy meeting the Which Lane accuracy, whereas the Type B receivers provided Which Road accuracy. Also, the use of WAAS with receiver Type B has yielded some absolute accuracy improvement. Limiting the constellation to 24 (B24W) did not significantly degrade accuracy in this case.

As a second step, V2V relative accuracy when the same receiver type was used was estimated, and the mean errors are shown in Figure 3B. Based on the mean error for each pair, all four receiver pairs were able to provide Where-in-Lane relative position accuracy. The geodetic grade Type A receiver pair (AW–AW) yields the best relative accuracy at around 0.5 meters relative 2D error. In comparison with the mean absolute errors, the V2V relative accuracy is greatly improved as a result of cancellation of correlated errors, indicating a high degree of correlation of absolute errors in receivers under these test conditions.

The relative accuracy with mixed receiver types or configurations was also estimated. With r
espect to receiver type mixes, the Type A receiver from vehicle 1 was used with the three Type B receivers in vehicle 2, yielding three combinations as AW–BW, AW–BNW, and AW–B24W. Mean error statistics for these three combinations and the combination of BW from vehicle 1 and B24W from the second vehicle are shown in Figure 3C. In comparison to the same type receiver pairing, this shows much larger mean errors. For instance, for all AW receiver mixes, the mean relative error is around 2 meters. Therefore, it is fair to conclude that error characteristics and modeling in the navigation solutions in receiver A and B are type-dependent, and they may not be compatible when a receiver mix is used. The BW–B24W combination does not show a significant increased mean error, indicating that the constellation difference in this test was not significant enough to result in an increased relative positioning error.

Figure 3A. Individual receiver absolute accuracy.

Figure 3B. Relative accuracy with same receiver type.

Figure 3C. Relative accuracy with receiver/configuration mix.

V2V Solution Availability

Availability statistics were generated for all accuracy categories (Which Road, Which Lane). At a more abstract level, solution availability statistics were also calculated for the DPOS and RTK methods. RTK solutions were defined as available whenever the software yielded a solution for that particular epoch. Data gaps in the RTK method could be caused by either communication failure due to, for example, a large truck entering the line of sight between vehicles, or one vehicle disappearing around a corner, or because insufficient observations from common satellites were available at the two vehicles. DPOS solutions, calculated in post-processing, were defined to be available whenever both receivers had observations from four or more satellites and were therefore able to calculate the necessary independent position solutions. While the two definitions of availability are not quite congruous, because only that for the RTK includes the possibility of communication failure, comparison of logs of data transmitted between the vehicles showed that out of approximately 45 hours of data, only 0.22 percent of missing RTK solutions could be attributed to failure of the DSRC link.

Figure 4 plots the distribution of GPS service outages observed by AW and BW receivers in individual vehicles in all of the test environments including deep urban. Here, as described for the DPOS method, an outage for a single receiver is identified on an epoch basis whenever the receiver has observations from less than four satellites. The total driving time included in this dataset is 45 hours and 4 minutes for each receiver. Figure 4 [deep urban] shows the same statistics for deep urban environment driving only, and this contains 1 hour and 40 minutes of driving for each receiver. The latter was selected specifically as this environment contained the most challenging conditions.

Figure 4. Distribution of GPS service outages for individual vehicles.

An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages lasted less than 30 seconds. However, when using the high-sensitivity enabled Type B receivers, 100 percent of the outages lasted less than 5 seconds. No significant outage difference is seen between the observations from the same receiver type in the two vehicles.

GPS service availability for V2V applications was calculated using two approaches for the two relative positioning methods. For the DPOS method, individual vehicle service availabilities were time-synchronized in post-mission, and V2V DPOS solution availability was estimated. Figure 5 compares V2V solution outages using both receiver types and both relative positioning methods.

Figure 5. Distribution of GPS service outages for V2V applications.

The DPOS method yields better solution availability statistics than RTK. With both receiver types, more than 95 percent of DPOS solution outages are less than 10 seconds. With the RTK method, relatively longer outages were observed, especially for Type B receivers. With Type A receivers, the difference is only significant for outages shorter than 30 seconds. For Type B receivers, larger percentages of longer RTK outages were observed; this can be potentially attributed to poor carrier-phase tracking loop performance of these receivers and the impact on RTK.

Using GNSS Data

We anticipated performance issues arising from receiver type and configuration incompatibilities going into the prototype development effort. We identified use of raw GPS measurements instead of the DPOS method as one method to overcome this limitation, as the differencing techniques with measurement data guarantees correlated error cancellation. This was one reason to include the RTK capability in the prototype system. Therefore, confirming the fact that use of raw measurements eliminates the receiver type and configuration-related incompatibilities was a major goal of the study.

As discussed earlier, V2V relative position solutions using RTK were logged in real time as a part of the test setup. We compared these real-time RTK solutions and the DPOS solutions estimated in post-mission for all datasets. Figure 6 shows three cumulative probability distribution (CDF) plots generated using RTK and DPOS accuracy data from a freeway test dataset. The first CDF plot (left) shows the comparison of accuracy when both vehicles use Type A receivers with RTK and DPOS methods. The second CDF plot (center) shows the same CDFs when both vehicles use the Type B receivers. The third shows the DPOS and RTK accuracy CDFs when vehicle 1 uses Type A receiver and the other uses Type B receiver.

Figure 6 demonstrates that if higher quality GPS receivers similar to Type A are used in both vehicles, both RTK and DPOS methods would provide a solution of better than Which Lane accuracy more than 90 percent of the time. However, if Type B receivers are used, a solution with similar accuracy will only be available 60 percent of the time if the DPOS method is used for relative positioning of the vehicles. If the RTK method is used, this availability can be increased up to 90 percent.

The performance difference between the two methods becomes even more prominent when the two vehicles use a mix of receiver types. In the right-most CDF of Figure 6, a solution with Which Lane accuracy is only available 30 percent of the time if DPOS method is used with the mixed receiver configuration. The RTK solution availability still remains around 90 percent even with the mixed configuration. This confirms that use of measurement data eliminates some of the limitations associated with the DPOS method.

Comparison of only the RTK performance between all three CDFs in Figure 6 shows that RTK V2V performance is only limited by the worst-performing receiver in the receiver combination. Out of the three CDFs, the middle (both vehicles using Type B) and the right (Type A and B mix) CDFs have almost identical RTK performance curves. Given that the RTK curve with both using Type A receivers shows much better performance, it is fair to conclude that in the mixed-receiver case, the RTK curve is limited by the performance of th
e Type B receiver. Figure 6 also shows that at Which Road accuracy, all receiver combinations and both processing methods yield almost identical performance.

Figure 6A. Comparison of V2V solutions using RTK and DPOS methods.

Figure 6B. Comparison of V2V solutions using RTK and DPOS methods.

Figure 6C. Comparison of V2V solutions using RTK and DPOS methods.

Other Approaches

Given that carrier-phase measurements are subject to cycle slips in some road environments, we ran a test using code measurements only in relative mode, using selected data sets collected on a mountainous highway. Only common satellites were used. Given that code measurements are not affected by a loss of phase lock, such a solution is more robust, but is subject to code noise and multipath. The RMS horizontal position differences between these solutions and the reference inter-vehicle separations were 25 centimeters and 1 meter for receiver Types A and B, respectively. Both receiver types meet the Where-in-Lane requirement in this test. Type A, with its low code noise and excellent code multipath-reduction capability, has a clear advantage.

Such an approach would represent a compromise between the DPOS and RTK approaches. Its advantage over the RTK approach is a lower data transmission-rate requirement, while that over the DPOS approach is the use of common satellites only. The latter is quite significant, since low-elevation satellites contribute the most to horizontal position solutions, but their measurements are affected more by atmospheric transmission errors that are most effectively removed in differential mode on a satellite-by-satellite basis.

V2V Operation with V2I

While infrastructure support can almost always improve the performance of other V2X applications, it can pose a challenge for positioning when such coverage is not continuous. The complication arises as a result of vehicles transitioning in and out of V2I coverage areas. V2I systems are highly likely to include GNSS augmentation capability so that vehicles within a coverage area benefit from better positioning capability. However, when vehicles transition from standard (V2V) operation mode to a V2I enhanced mode, some effects in the vehicle position domain can pose potential challenges for DPOS-based V2V.

The field study included test scenarios with limited V2I coverage in different driving environments: all of those described above with the exceptions of deep urban and mountains. In deployment, the infrastructure points (IPs) would broadcast aiding information to the vehicles within their coverage area, allowing real-time calculations. In the field study, in which the role of the IP was filled by a stationary high-grade receiver with a tripod-mounted antenna, all V2I estimates of the IVV were calculated using post-processing. Further, V2I estimates of the IVV were only calculated when at least one of the vehicles was within the coverage area of the IP, here chosen to be a circle of radius 300 meters centered at the IP. This range was chosen since it is the nominal effective range of the DSRC link.

The location of the IP, that is, the phase center of the stationary antenna, was determined using commercial RTK network software with additional stations at precise locations on the rooftop of a building at the University of Calgary. The estimated accuracy of this position was 5 millimeters (1 sigma). The distances of the vehicles from the IP, used to indicate when the vehicles transitioned into and out of the IP coverage area, were determined using the GPS/INS reference trajectories. In post-processing, once a vehicle was identified as having entered the IP coverage area, commercial RTK software was used to estimate the position of the vehicle, using the IP as base and each of the test receivers on that vehicle as rovers. The IVV was then calculated using the difference of the positions of the two vehicles. Thus, the V2I estimate of the IVV was determined using what is essentially the DPOS method with stationary base RTK-indicated vehicular positions, instead of the less accurate single-point GPS position solutions. When only one vehicle was within the coverage area, single-point solutions were used for the distal vehicle, resulting in a solution called V2I-S.

Figure 7 shows two sets of CDFs generated to illustrate the V2V positioning accuracy with V2I capability. The left plot corresponds to AW–AW receiver combination, and the right plot corresponds to the BW–BW combination. Each plot includes four curves. One pair of curves shows the V2V positioning accuracy without V2I, which includes performance when using the DPOS method (green) and another when using RTK (blue). The second pair shows the accuracy of the V2I and V2I-S estimates.

The most striking observation from Figure 7 is the separation of the V2I-S case from others for both receiver combinations (purple). It shows much worse positioning accuracy compared to the other three curves. For instance, using a BW–BW pair, the system will meet the Which Lane accuracy requirement around 80 percent of the time for either DPOS or RTK V2V without V2I support. However, when V2I coverage is available to only one vehicle, the V2I-S case, the accuracy requirement is only met at 40 percent confidence.

Figure 7A. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).

Figure 7B. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).

Thus, system accuracy performance degrades when vehicles are operating in DPOS mode and are transitioning in and out of the V2I zones. This is because the V2I-S estimate is the difference of an accurate position solution for the vehicle within the coverage zone, and a potentially inaccurate single-point solution for the one outside the coverage zone. The beneficial cancellation of similar errors that occurs for DPOS estimates (using similar receivers and with common satellite observations) does not occur for V2I-S.

Potential solutions to this problem include using a V2I method of IVV calculation that is not dependent on the estimated position alone (that is, use RTK or other measurement-based methods as opposed to DPOS), or using a position-mode indicator with the DPOS mode such that a DPOS-based V2V solution is only generated when both vehicles are operating in the same mode (that is, V2I). However, the latter does not provide a remedy for the complications when the two vehicles are operating in two different modes. One could also consider a variation of the latter method whereby a V2I-augmented position and a non-augmented position is maintained by each vehicle, such that one of them could be used to generated a mode-matched DPOS V2V solution for a given sender.

Recommendations

These extensive trials provided valuable data demonstrating technical challenges associated with V2X positioning.
- Error characteristics and modeling in the navigation solutions in receivers A and B are type-dependent, and they may not be compatible when a receiver mix is used with the DPOS mode. This is very likely to be the case for many other commercial receivers. Therefore, it is important to develop receiver hardware and software minimum-performance standards that define acceptable performance for measurement quality, satellite tracking and selection criteria, reliability estimates, navigation-solution parameters, and other such indicators.
- Findings with RTK confirm the fact that use of measurement data eliminates some of the limitations associated with the DPOS method. While RTK is the most demanding raw data-based method in terms of processin
  g requirements and OTA data needs, the study also conducted limited investigation on other methods that use raw code data and are less resource-intensive, and at the same time better performing than DPOS. Such an approach would represent a compromise between the DPOS and RTK approaches.
- An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages were less than 30 seconds. These statistics are useful for future research on suitable GNSS augmentation methods.
- System accuracy performance degrades when vehicles operate in DPOS mode and transition in and out of the V2I zones. Potential solutions should be incorporated into the systems to take care of these limitations.
Acknowledgments

The authors thank the Crash Avoidance Metrics Partnership Vehicle Safety Communications-Applications team, in particular the Vehicle Positioning Technology Development team, for input. This work was conducted as a part of a CAMP VSC-A project under a cooperative agreement with the U.S. DOT.

CHAMINDA BASNYAKE is a senior research engineer at General Motors Global Research and Development and GNSS technology expert for GM OnStar. He leads GNSS-based vehicle navigation technology R&D efforts at GM and holds a Ph.D. in geomatics engineering from the University of Calgary.

TOM WILLIAMS is a postdoctoral researcher in the PLAN group in the Department of Geomatics Engineering at the University of Calgary.

PAUL ALVES is a Calgary-based geomatics consultant specializing in RTK. He obtained his doctorate from the University of Calgary.

GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.
October 1, 2010
INRIX Expands the Largest Traffic Network in Europe

INRIX announced it has expanded its European real-time traffic coverage to 18 countries making it the largest traffic network in Europe. With the launch of real-time traffic information in Ireland, Hungary, Poland and Slovenia since February, INRIX traffic services now cover more than 1 million kilometers of motorways, city streets and secondary roads, throughout Europe — more than 2X the amount of real-time road coverage of its nearest competitor.

“Whether driving across town or across borders, European customers uniquely benefit from INRIXs ability to reliably help drivers avoid traffic congestion wherever their travels take them,” said General Manager of INRIX Europe Dr. Hans-Hendrik Puvogel. “Through our expanded coverage, continuous technology innovation in support of standards like TPEG over IP, and growing customer base, we’re proving to the market everyday why we’re the best provider of quality traffic services across Europe.”

In a separate announcement today, INRIX introduced a breakthrough in the delivery of traffic information called TPEG Connect. Based on the new encoding and transmission standard for traffic and travel information developed by the Transport Protocol Experts Group (TPEG), INRIX TPEG Connect provides automakers and navigation application providers with the ability to optimize payloads and bandwidth for delivering richer real-time and predictive traffic flow, incident, and location-based services like weather conditions on the road to devices using TPEG over IP. By providing delta support that can reduce data payloads by up to 50 percent on each message request, INRIX TPEG Connect helps OEMs and consumers save on connectivity costs by reducing data consumption in ways that ensures only the most location-relevant real-time information is delivered to the device.

“TPEG Connect provides the industry with a better way to deliver pan-European traffic information that enables the delivery of more dynamic traffic and traveler information at less cost both to the OEM as well as the consumer,” said INRIX Vice President of Product Management Ken Kranseler, “By making the standard production for use over IP, INRIX TPEG Connect removes key technical and commercial hurdles for our customers accelerating the delivery of next generation of traffic applications and driver services that will improve mobility for millions of people worldwide.”

According to the announcement, INRIX delivers the broadest and most accurate real-time traffic information through its distinctive Smart Driver crowd-sourced traffic information network and Total Fusion data analytics technologies. The company offers real time traffic information today in the following European countries:

Austria
Belgium
Denmark
Finland
France
Germany
Hungary
Italy
Ireland
Luxembourg
The Netherlands
Norway
Poland
Spain
Sweden
Switzerland
Slovenia
United Kingdom

INRIX also announced an agreement with road safety products and services company Coyote Systems to provide real-time traffic information in future Coyote products. As Coyote’s preferred global provider of traffic information, INRIX and Coyote will work together to apply each other’s expertise in user-generated content for the development of future products and services.

July 28, 2010
Kick It in and Push!

By Alan Cameron

The Elephant Charge (“Dust, Sweat, and Gears”), an annual off-road motorsport charity event, brings together competitors, their families, and supporters for a wilderness weekend of GPS-driven fun and frenzy in the Zambian bush. I’m for fun, but I always wince when I see folks tearing up habitat in the name of saving it.

Elephant Charge 2010 seeks to raise funds and awareness for local conservation in Zambia, specifically for two hides, or wildlife observation posts, in Lusaka National Park along with funding for the South Luangwa, Lower Zambezi and Kafue National Parks private-sector conservation efforts. Organizers hope to attract more than 300 campers over the weekend of October 23–25 and as many day observers and participants, en route to a fundraising goal of $35,000.

Focus of the weekend is an event for car and motorbike teams that requires stamina, sweat, driving, and navigation skills through the Zambian bush. Maps showing the location and GPS coordinates of nine checkpoints are issued to teams on the evening before the race. To win, a team must complete the nine-checkpoint course in the shortest distance among competitors. Each team finds it own route between the checkpoints, in any order, through valleys, over ridges, and up (or down) escarpments. The goal of short distance explicitly encourages teams to go off-road in their vehicles. Bush roads are cut to each checkpoint and marked on the issued maps, however they never give the shortest distance.

The blog piece you are reading is armchair bushwhacking at best, and it’s hard for me to preach at a distance to Zambians on how to use, exploit, preserve, or tear up their own turf. Of course it’s heartening to see GPS enlisted in conservation and education efforts. I just wish they weren’t harming habitat — by cutting bush roads and further encouraging racers to rip off through the vegetation — in order to help preserve it.

Visit www.elephantcharge.org for more information.

Alternatively, for a terrific vicarious experience of the Africa savannahs and bush without leaving home, read either Don’t Let’s Go to the Dogs Tonight by Alexandra Fuller, set in Rhodesia, Zambia, and Malawi, or Sand Rivers by Peter Mathiessen, set in Tanzania. “The crack of the dry grass, the intense heat, the startling beauty of the birds, the fleeting glimpse of wary wildlife . . .”

July 13, 2010
Elbow Room on the Shoulder: DGPS-Based Lane-Keeping Enlists Laser Scanners for Safety and Efficiency
A virtual reference station network covering a metropolitan area supplies position corrections to commuter buses equipped with a driver-assist system to enable safe operation, even under harsh weather conditions, along high-volume roadways.

By Craig Shankwitz

Bus-only shoulders on major traffic arteries enable a bus to travel on typically unused road right-of-way, bypassing congestion during peak rush hours. As the shoulder is typically only centimeters wider than the bus itself, lane-keeping becomes a key factor, and is accomplished in a pilot Minnesota project using dual-frequency, carrier-phase differential GPS (DGPS) as its primary positioning technology. DGPS provides position estimates accurate to 5–8 centimeters at a rate of 10 Hz, and is used to determine vehicle position and heading. An on-board map database is used to determine the position, orientation, and trajectory of the vehicle relative to the roadway.

Use of the shoulder as a busway offers several construction and operational advantages:
- Ease of Implementation. The shoulder exists; there is no need to acquire and develop additional right of way.
- Low Costs. The cost to strengthen and modify an existing road shoulder is significantly less than constructing a new busway.
- Routing. Because bus-only shoulders follow existing routes, no changes to bus routes, bus stops, or transit stations are needed to support bus-only shoulder operations.
- Customer Satisfaction. Transit customers who travel on buses that use a bus-only shoulder perceive a travel-time saving two to three times greater than actually realized. Keeping the bus moving at all times offers a significant psychological advantage.
- Increased Ridership. A 1997 study of bus-only shoulders in the Twin Cities analyzed more than nine bus-only shoulder routes for two years and found a 9.2-percent increase in ridership along these routes. At the same time, total ridership had decreased by 6.5 percent.
However, the use of bus-only shoulders imposes additional stress and strain on a driver. The narrow bus-only shoulder leaves a driver very little margin of error. Operating within this small margin is difficult even during the best traffic and weather conditions, and degrades to nearly impossible during heavy traffic and poor weather conditions, which are frequent during Minnesota’s notoriously hard winters.

During difficult weather and traffic conditions, the use of the bus-only shoulder offers its greatest transit advantage. If a driver is unable to utilize the bus-only shoulder, this advantage is lost. A properly designed and executed driver-assist system (DAS) enables a driver to use the shoulder under all conditions, thereby increasing schedule adherence and, as a result, rider satisfaction.

Under the U.S. Department of Transportation’s Urban Partnership Agreement, the University of Minnesota’s Intelligent Vehicles Lab (IV Lab) and HumanFIRST program, the Minnesota Valley Transit Authority (MVTA), and Schmitty and Sons Transportation will soon deploy DAS on 10 Gillig low-floor transit buses. These buses will provide express service between Apple Valley and downtown Minneapolis, a 22-mile, one-way trip.

Driver-Assist History

The IV Lab has developed and deployed DGPS-based DAS since 1995. The first deployment on public roads occurred in 2001, as part of the DOT’s Intelligent Vehicle Initiative Generation Zero Field Operational Test. The DGPS-based lane-keeping assistance was integrated with forward-looking radar for collision avoidance, enabling safe vehicle operation in zero-visibility conditions.

Two separate deployments took place in Alaska. The first occurred in 2003 with a snowplow and a snowblower which clear the Thompson Pass on the Richardson Highway. These vehicles are still in use. Because of this success, the State of Alaska installed the DAS in two more vehicles at Deadhorse Airport.

During the summer of 2010, the two original Thompson Pass systems will be upgraded with new computational hardware, and three new systems will be installed on three new highway maintenance vehicles. The value of the driver-assist system has been proven, and those who use it have grown to rely on its all-weather capabilities. It has functioned reliably for seven years in extremely harsh conditions.

DAS-EQUIPPED SNOWPLOW clearing Thompson Pass, Alaska.

Driver-Assist for Transit

The DAS provides two primary capabilities for transit applications: lane-keeping and collision awareness. The system provides assistance only; a driver is always responsible for control of the vehicle. Figure 1 shows the components comprising the DAS.

Figure 1. Complete driver assist system component schematic, showing both infrastructure-based and vehicle-based components.

DGPS-Based Lane-Keeping. The primary positioning sensor used aboard the buses is a dual-frequency, carrier-phase GNSS receiver, providing centimeter-accurate position measurements at 10 Hz. With the exception of the DGPS augmentation system described later, all other DAS system processes are synchronized with the arrival of DGPS position updates.

Realtime CMR+ DGPS corrections are provided over the 3G cellular network from the IV Lab VRS network. The IV Lab VRS network is based on six receivers located around the perimeter of the Twin Cities Metro area. These six receivers are connected via landlines to a server system located in the IV Lab at the University of Minnesota, running GPSnet and RTKnet applications. To ensure GPS correction reliability, an integrity manager software issues alerts for both short-term and long-term aberrations in the data provided by the six base stations. This ensures accurate corrections are sent to the buses using the narrow shoulders.

The onboard receiver also plays a crucial role in accurately estimating vehicle body heading. In rural applications where GPS augmentation is unnecessary, GPS velocity heading estimates provided directly from a GPS receiver serve as a sufficiently accurate body-heading estimate. However, in GPS-denied environments where an augmentation system is needed to provide accurate position and heading estimates when GPS is lost, velocity heading from an onboard receiver is an insufficiently accurate estimate of vehicle heading. To support such navigation, the IV Lab developed a technique, described later, by which body heading can be estimated with errors less than 0.1 degree.

IV Lab mapping rig installed in a pickup truck: three dual-frequency, carrier-phase DGPS receivers; two laser scanners, one measuring retroreflectivity, the other road crown and rutting; and forward and sideview cameras, to help analyze anomalous data.

Map Databases

Lane-keeping uses DGPS with an onboard map database describing the location and type of lane boundaries and other relevant roadway elements to an accuracy of approximately 10 centimeters. These map databases can be constructed in one of three ways:
- from sufficiently accurate photogrammetric data,
- by driving centerlines and using known road-construction standards to d
  etermine the location of lane boundaries and other relevant elements relative to the lane centerline, or
- by using a combination of laser scanners, DGPS receivers, and cameras to determine the global location of the reflective markings that bound lanes and shoulders.
Lane-keeping information is continuously provided to the driver; lane-departure alerts and warnings use a comparison of vehicle speed and heading to the map database to determine when alerts and warnings should be issued.

The alerts and warnings are provided via a multi-modal human-machine interface (HMI), illustrated in Figure 2, through three modes:
- graphically, through a head-up display (HUD) that gives a virtual view out the windshield when environmental conditions limit visibility;
- haptically, through a torque-actuated steering wheel giving a restorative torque on the steering wheel in the event of lane drift; and
- tactically, through a seat equipped with actuators that vibrate on the side of the seat to which the lane is being departed.
Figure 2. Multi-modal driver interfaces. Left: Graphical, haptic, and tactile feedback modes provided to the driver. Upper right: View through the head-up display. Graphical lane departure alert indicated by left shoulder boundary colored red, collision awareness alert (white rectangles), and collision awareness warning (red rectangle). Lower right: Forward, left, and right side collision awareness information presented on the display on the left “A” pillar.

Lane-departure warnings come in stages. As the vehicle-trajectory estimator determines that the likelihood of a lane departure is sufficiently high, a lane-departure warning is issued to the driver through the HUD: a change in lane boundary color from white or yellow to red. Should the driver contact the lane boundary, a seat-based warning is activated; the side of the seat corresponding to the direction of lane departure vibrates, warning the driver. If the driver fails to respond to these two stimuli and continues past the lane boundary, the steering motor torque is applied. This multi-stage approach captures the drivers’ attention, but if they respond in a timely fashion, their annoyance is limited.

The torque applied by the steering servo motor is limited, and cannot deliver sufficient control action to autonomously steer the vehicle. This is by design; the driver is responsible for operating the bus. The level of torque applied to the steering wheel is analogous to an automotive front-end misalignment; it is sufficient to capture the drivers’ attention, but not to steer a bus off the road.

Forward-Collision Awareness. Sensing for forward-collision assistance is provided by a front bumper-mounted multi-plane scanning LIDAR sensor. Forward-collision alert and warning information is provided in two stages to the driver through the HUD. As now configured, if the obstacle detected is in the present shoulder of travel, the obstacle is represented as a red, open rectangle, with red indicating a warning status. If an object is located in an adjacent lane, the obstacle is represented as a white, open rectangle, with white indicating an alert status.

Obstacle-detection processing is enhanced by the presence of the onboard map database used for lane-keeping. Obstacle target information provided by the LIDAR sensor includes range, range rate, and azimuth angle to the target. The bus position and heading is provided by either DGPS or the DGPS augmentation system. Through a coordinate transformation, LIDAR information in the vehicle coordinate frame is transferred to the global coordinate frame. This allows the LIDAR target to be placed on the map database; if the target is in the vehicle lane of travel, it can be considered a threat, but if the LIDAR target is not in the same lane as the bus, then at that time the target is not a threat to the driver.

Side-Collision Awareness. Side collision awareness is enhanced by multi-plane LIDAR scanners mounted on on the front bumpers on both the left and right sides of the bus, and connected to a pneumatic actuator.

Side-collision awareness information is provided to the driver via an LCD panel mounted on the left front A-Pillar (see Figure 2). This display is touch-sensitive, and can be used by the driver to log in (only certified, trained drivers can operate the system) to select feedback modalities (choose any or all of the available feedback modes) and to check system status.

SIDE-MOUNTED LASER SCANNER used for both side-collision awareness and DGPS augmentation. When extended, the LIDAR scans 100 degrees of the horizontal plane. One boundary of the scanned plane points behind and runs alongside the bus; the other boundary points forward of the bus by approximately 10 degrees.

SIDE-MOUNTED LASER SCANNER used for both side-collision awareness and DGPS augmentation. When retracted (right), the LIDAR points in the direction of the ground, and can be used for curb-following when DGPS is unavailable.

Suburban and Urban

Although the rural implementation of the DAS operates in extremely harsh weather conditions, these implementations are technically less problematic than suburban and urban implementations. In rural applications such as the snowplows, DAS-equipped vehicles typically operate with a single occupant in a small geographic area, travel on relatively low traffic-volume roads, and enjoy a clear view of the sky. Suburban and urban applications carry passengers, operate across a wider geographic area, travel on high-volume roads, and suffer from periods where view of GPS satellites is either partially or completely blocked.

These operational differences require substantial changes to the DAS subsystems for urban/suburban use.

DGPS Base Stations. In rural areas, DAS-equipped vehicles typically operate over a relatively small geographic area; a single GPS base station will provide adequate coverage as the maximum baseline between rover and the base station remains less than 25 miles. Suburban applications cover a much wider area, and a network of DGPS correction stations is needed to keep baselines low.

For the UPA project, the IV Lab operates a six-station virtual reference station (VRS) network. This network covers the greater Twin Cities Metropolitan area, and supplies compact measurement record (CMR) corrections to each DAS-equipped bus. Satellite observables are sent from each base station receiver to both the VRS server at the IV Lab and to a VRS server at the Minnesota Department of Transportation.

Broadcast of DGPS Corrections. In rural areas, the DAS system has served to keep roads passable in inclement weather conditions. This has been viewed as a safety application, and as such either UHF or VHF channels in the public safety bands have been used to broadcast DGPS corrections. In urban areas, no single UHF or VHF frequency is available to cover an entire metropolitan area. Therefore 3G cellular data communications are used to provide DGPS corrections to DAS-equipped vehicles.

Use of 3G cellular data communications brings the transit customer an added benefit: free Wi-Fi. The provision of DGPS corrections, using the CMR+ correction format, requires approximately 10 Kbit/second. This bandwidth is assigned high priority by the onboard router. The remaining 700 Kbit/s of 3G bandwidth is made available, at a lower priority, to bus passengers. On an express route service, passengers can e-mail and surf the web on their daily commute, making productive use of
time that might otherwise be lost.

The VRS server provides a unique correction to each DAS-equipped bus. Communication between the bus and the VRS server is initiated by the bus when it sends its coarse (uncorrected) position to the server. The server replies with a correction optimized for that coarse location. Corrections are sent at one-second intervals. Every two minutes, the bus sends its current position, and the VRS server responds with corrections optimized for that new location. With this scheme, the baseline between the VRS and the roving bus is never more than two miles. The two-mile limit maintains position accuracy without consuming excessive wireless or computational bandwidth.

DGPS Redundancy. In rural applications, the view of the sky is generally unobstructed, and FCC licenses provide adequate effective radiated power from the DGPS base stations. This assurance of access to both satellite and corrections signals generally suffices to support uninterrupted vehicle positioning. Both base-station and onboard GPS hardware have proven to be robust and reliable. With these local operating conditions, public agencies have found no need to augment DGPS for rural applications.

Suburban and urban applications, however, require an augmentation system to support DAS operation when DGPS is unavailable due to outages caused by overpasses, overhead road signs, tree canopies, and so on. Passenger safety and the need to provide reliable schedule adherence require that positioning be provided even when DGPS is unavailable, by a vehicle-based DGPS augmentation system.

Vehicle-Based Augmentation

The vehicle-based augmentation system (VBAS) uses direct measurements of ground velocity, a measure of vehicle yaw rate, and an accurate estimate of the vehicle position and heading at the time DGPS is lost to estimate vehicle position and heading for the duration of signal loss.

A commercial off-the-shelf sensor designed for measuring vehicle and/or tire slip measures vehicle 2D velocity. Yaw rate can be measured either with an inertial rotational rate sensor or a second 2D velocity sensor. Yaw rate measured using a pair of these 2D sensors eliminates the rate bias and rate bias drift associated with inertial sensors. Figure 3 shows both configurations.

FIGURE 3 Two approaches to VBAS to mitigate DGPS outages. The diagram on left shows implementation with two 2D velocity sensors to determine vehicle yaw rate. Computationally, this is attractive as senor drift need not be considered. The diagram on the right shows an implementation with one yaw rate sensor, and one 2D velocity sensor. This is the configuration operating for the UPA; it requires yaw rate sensor drift compensation to provide accurate measures of vehicle yaw rate.

An accurate measure of vehicle heading at the time GPS positioning is lost is critical to the augmentation process. A performance goal of 20 centimeters tolerable error at the end of a 15-second outage for a vehicle traveling at 25 miles per hour (11.2 meters/second) requires a heading estimation error of no more than 0.07 degrees (that assumes the only source of error is attributable to the heading).

GPS outages (time from loss of position to reacquisition) attributed to passing under overpasses range from 7 seconds (single bridge) to 9 seconds (double bridge). The IV Lab augmentation system reliably provides sufficiently accurate position and heading estimates to carry through these outages. At the present level of performance, should an outage last more than 15 seconds, the accuracy of the augmentation system cannot be guaranteed. In this event, the driver is alerted, and the DAS is deactivated until a DGPS position fix is reacquired. Fortunately, since new receiver firmware was installed, no instances of an outage exceeding 15 seconds have occurred during two months of test, evaluation, and driver training.

Figure 4 illustrates the accuracy of the VBAS system. At the time the fix solution is reacquired on the exit ramp, the lateral error between the fix solution and the position estimated by the VBAS is approximately 10 centimeters. This accuracy is sufficient to allow a driver to travel on the entrance ramp even during zero-visibility conditions.

Figure 4. Example of VBAS as a bus operates on the Cedar Avenue/Old Shakopee Road overpass. Bus trajectory is northbound on Cedar, exiting westbound Old Shakopee Road, then entering southbound Cedar Avenue from Old Shakopee Road. Upper left shows northbound trajectory and loss of satellite lock. Upper right shows reacquisition of DGPS, float, and fix states of the DGPS receiver. Lower right shows accuracy of VBAS system compared to DGPS when DGPS reacquires fix. Lateral error of VBAS at at the time the fix is reacquired is approximately 10 centimeters. Lower left shows satellite view of the interchange.

Driver Training

Bus-only shoulder operation has proven itself safe and, in fact, safer than normal transit operations, according to recent data. The goal of driver training is to prepare drivers to use the DAS system to enable them to safely use the bus-only shoulders in conditions under which they normally would not.

A rigorous training protocol developed in cooperation with the University of Minnesota HumanFIRST program, Schmitty and Sons Transportation driving instructors, and MVTA involves both simulator-based and on-road training.

Simulator-Based Training

Beefore using driver assist systems, bus drivers are continually taught that the driver controls the bus and is responsible for both the passengers and vehicle. Drivers take this responsibility seriously, and as such, develop skills and techniques that guarantee safe passage under all conditions, even when running on narrow, bus-only shoulders.

To best prepare drivers for using the DAS under difficult conditions, a high-fidelity driving simulator was commissioned. A DAS was installed in the simulator, and an interface to the simulator was created. In this context, a driver has the ability to train in normal and abnormal (low to zero visibility) conditions before beginning on-road DAS training and use.

In the simulator, the driver learns that the system only provides assistance; responsibility for the safety of the bus and passengers still resides with the driver. Experience with Alaskan snowplow operations, where formal training is limited to a few on-road test drives, has shown that a driver may take a few winter seasons to fully accept the system. This delayed acceptance is in part attributable to the fact that for six months per year a driver has no opportunity to train with the system. Acceptance gained over one winter season is lost during the summer.

The simulator installed at an MVTA bus garage uses a seat-based motion platform to achieve realistic vehicle dynamics. The DAS installed in the simulator allows a driver to train in all weather and traffic conditions on a geospecific roadway before transitioning to a DAS-equipped bus. Geospecificity is achieved through the creation of virtual worlds based on roadway data collected by the mapping vehicle shown earlier.

Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.

Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.

Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.

Bus-driving simulator at the MVTA bus garage in Burnsville, Minnesota.

On-Road Training

After a driver both demonstrates an
d acknowledges comfort and competence with the DAS in the simulator, training transitions to the actual route on which the buses will operate. Each of the 10 buses is equipped with a six-camera data-acquisition system. The six cameras capture not only the driver’s actions (hands, face, feet), but also views of the road (front, left, and right sides.)

Drivers travel with an instructor. The onboard data acquisition system can be used to reconstruct particular scenarios as a means to offer advice as to how the driver and system can better interact in difficult driving and traffic conditions.

On-road training benefits system developers as well. Training offers a driver an opportunity to test the system in real-time on an actual road. The perspective a driver brings is generally different than that of the developer, and the insights the end user provides typically produce a better system. As an example, driver experience with the system during the initial training period produced the staged approach to lane-departure alerts previously described.

Conclusion

The IV Lab, MVTA, and Schmitty and Sons Transportation will soon release 10 DAS-equipped buses into revenue service to support narrow bus-only shoulder service between downtown Minneapolis and Apple Valley, Minnesota. Although the IV Lab has deployed a number of DAS-equipped vehicles, this UPA deployment represents the first time that the system has been used to transport passengers. This deployment should prove that although DGPS systems are susceptible to periodic outages, a properly designed and executed augmentation system will provide a sufficiently robust system that will be accepted by both drivers and passengers. It will also demonstrate to other transit agencies that even narrow rights of way offer significant transit advantages at low cost, and that potential operational difficulties can be overcome through the use of DAS technologies.

Manufacturers

The buses carry Trimble R7 receivers and Ibeo Lux multi-plane scanning LIDAR sensors. The IV Lab VRS network is based on six Trimble NetR5 receivers. The server runs Trimble’s GPSnet and RTKnet applications, with the Trimble Integrity Manager.

Craig Shankwitz is the director of the Intelligent Vehicles Laboratory at the University of Minnesota.
July 1, 2010
INRIX Announces INRIX Traffic! and INRIX Traffic! Pro Availability for iPad

INRIX announced the upcoming release of a new iPad version of INRIX Traffic!, its popular app for commuters.

Using the MDK (mobile developer kit), INRIX completed development of an iPad optimized version of its popular INRIX Traffic! and INRIX Traffic! Pro app in less than 2 weeks. Coming later this month to the iPad App Store, INRIX Traffic! is a free app that provides real-time traffic, traffic forecast, speed trap, accident and incident information for all major cities and roads across the U.S. and Canada. Winner of a 2010 MacWorld Best of Show Award, INRIX Traffic! Pro is available as an in-app upgrade to the free app that provides motorists with the added benefit of always knowing the fastest route, best time to leave, travel time and ETA for any destination.

“Our mobile apps and tools have helped companies like Ford and providers of 8 of the top 10 most popular GPS smartphone navigation apps get to market fast with new traffic-powered navigation services,” said INRIX President and CEO Bryan Mistele. “Bill’s experience helps us transform our unique consumer insights into new features that extend beyond INRIX Traffic! to apps that empower our partners and customers to deliver consumer experiences that make navigation more useful every day.”

June 9, 2010
INRIX’s Crowd-Sourced Traffic Network Surpasses 2 Million Vehicles

INRIX announced its Smart Driver Network has grown to more than 2 million GPS-enabled vehicles giving drivers a reliable, real-time view of traffic conditions on more than 260,000 miles of highways, city streets and secondary roads nationwide.

“Our Smart Driver Network is the largest real-time traffic network in the world. It redefines what it means to deliver truly real-time traffic information,” said INRIX President and CEO Bryan Mistele.

According to the announcement, more than 40 percent of all State DOTs in the United States rely on INRIX’s real-time traffic information for their daily operations, traveler information services and/or congestion performance measures. New projects in 2010 in 5 states – Texas, Massachusetts, Maryland, Minnesota and Ohio – are using INRIX traffic data and travel times for their planning efforts, statewide 511 systems or dynamic message signs.

“In just two years, INRIX has grown from providing traffic data for one state agency to powering the daily operations, planning or traveler information services in 21 states and the District of Columbia,” said INRIX Vice President of Public Sector Rick Schuman.

May 7, 2010
On the Edge: Got a Fast Car

A team will attempt to shatter the world land speed record, with a GPS/GLONASS receiver riding the controls.

photo: GPS/GLONASS

In summer 2010, a team of 44 volunteers will attempt to shatter the world land speed record of 763 miles per hour (2 mph faster than sound) by hitting 800 on the speedometer. To  ensure that the North American Eagle takes it to the limit efficiently and safely, the team captures performance data from 70 sensors, locked to position,  velocity, and time coordinates by an onboard GNSS receiver.

An old Lockheed F-104, a ’60s era Mach II fighter, rescued from a scrap dealer in Maine seemed to have the mark of greatness somehow still on it, amid the fuselage holes and grafitti. A team of volunteers converted the plane into a supersonic car: one expert machined the solid billet aluminum wheels, another rebuilt the General Electric J-79 engine, another devised a magnetic braking system. Over three years, the team replaced 40 percent of the plane’s skin panels, 5,000 rivets, the front suspension, and the steering and hydraulics systems.

The 56-feet-long, 13,000-pound car features Magna Force Lev-X magnetic brakes, a stock engine that outputs 42,500 horsepower, burning 160 gallons of fuel per minute in afterburner mode, and a backup 52,000-hp engine.

At supersonic speeds, anticipating and controlling the car’s reactions to physical stresses become critical. explains Steve Wallace, data-acquisition engineer: “Stuff happens when you get up to the speed of sound. Shock waves affect the aerodynamic balance of the vehicle, and when you’re flying six inches from the ground, aerodynamic unbalance becomes very important.” Wallace and the team must ensure that the car does not burrow into the ground or lift any of its wheels while in motion; either would be detrimental to record-setting and driver safety.

Accelerometers, strain gauges, piezoelectric sensors, inertial gyros, airspeed and air-pressure sensors, and more all generate streams of data, stored on a laptop computer mounted behind the car’s cockpit. Wallace accesses the system in real time via an Ethernet wireless umbrella of routers mounted on 20-foot-tall towers spaced at 2½-mile intervals alongside a 14-mile track at Black Rock Desert, Nevada. After a test run, he downloads the data to his computer, correlates time and location, then exports to a spreadsheet.

GNSS makes speed measurement more reliable and accurate. “We have a lot of sensors, but one sensor I don’t have is for vehicle speed,” he says. “This is a thrust vehicle; the wheels aren’t driven, so realistically, the wheels are never going as fast as the ground. Measuring airspeed is not a bad way of [measuring vehicle speed], but it’s very noisy. The data are all over the place and you have to do a lot of smoothing and averaging. With GNSS, it’s dead nuts — a great way of getting information I need, fast, without looking at accelerometer data. I can’t think of a more valuable tool to understand what’s happening from a sense of motion of the vehicle in general.

“If we had a $10 million budget, we could buy a ground-tracking radar unit like the Air Force uses, but that ain’t gonna happen. We can get just as good data with this as with a $10 million system, so why go any further?”

The project could provide further practical engineering benefits. The team likens this research to that of the 1960s space program, which benefited development of computers, cellular phones, and microwave ovens.

Manufacturers

A dual-frequency Topcon PG-A1 antenna and Euro 160T OEM receiver collect GPS/GLONASS signals at 20 Hz. A Euro 160T mobile control board rides in the car’s electronics bay. A GB-1000 receiver collects static reference data.

January 1, 2010
Expert Advice: Availability Gaps: Solutions for Aviation
Directions 2010

James L. Farrell

By James L. Farrell

Recent attention given to aging GPS satellites and availability gaps from lagging constellation replenishment have provoked deep concern, particularly within the aviation community. Available remedies include exploitation of well known but unused methods plus new techniques; those discussed here have future relevance, with or without availability gaps.

Even with far greater coverage from multiple GNSS, crises could emerge from severely stronger interference levels or other unforeseen events. Advance preparation for any such occurrence would avoid the waste, confusion, and blind alleys that generally arise with the sudden appearance of an emergency.

GPS lives up to expectations, brilliantly performing as advertised. Even that best-ever performance must and does have tolerance for occasional error; examples, though rare, are well documented. To live with less than perfect performance, the industry relies on integrity testing: comparison checks using extra satellites to detect inconsistencies and exclude questionable data.

Nevertheless, it is universally recognized that GNSS, even with existing fault detection and isolation or exclusion (FDI/FDE), is still not perfect. The ramifications of growing dependence on GPS have thus attracted more attention. The overall subject can be subdivided into general areas involving the likelihood of:
- reduced availability and
- reduced dependability (integrity, its verification, plus backup).
Although I mainly address the first topic here, the second unavoidably intertwines itself, making it difficult to keep them separate. Despite wide acclaim for the excellent 2001 Volpe Report, commitment to a key means of backup for GPS remains unclear at this time. Possibility of a shortfall calls for a review of both existing methods and procedures, and possible means for closing the gap.

Current Methods

Today’s air traffic management designs demand constant replenishment of instantaneous position by full fixes.

Full Fix 1 RAIM. When each data vector must be a self-sufficient source of instantaneous position, a requirement arises for enough satellite sightline directions with geometric spread at all times. That interdependence is magnified when more satellites are added to provide FDI/FDE, requiring every subset of four within the enlarged group to support the requisite geometry. With this all-or-nothing posture, data lapses form a major stumbling block. A data gap that is only partial equates to a loss of GPS.

Position-Oriented Approach. Especially at high speeds, as in flight, instantaneous position is highly perishable. With little or no emphasis placed on accurate dynamics (beginning with velocity), demand for continuously accurate instantaneous position is highly dependent on abundant data. That abundance includes sufficiently high data rates, since latency becomes a significant liability without usage of a dynamic file.

Carrier Phase (Classical). Successful use of carrier-phase information is decades old. Although ambiguity resolution is not required in all carrier-phase applications, requirements for cycle-slip detection are quite common. More common yet — in fact, virtually ubiquitous — is the need to maintain phase continuity via a carrier-track loop. When those needs are satisfied, sub-wavelength instantaneous position is obtainable. Challenges involved, however, have produced among users a wide variation in perception of value. Some negative perceptions have arisen due to cutting corners in formation of carrier phase, or merely settling for delta range, by some receivers. Further, a cycle slip, even if only rarely overlooked, can be catastrophic in some operations.

Imperfect Validation. As already noted, verification is not my main topic here, but the issue is inescapable. Shortcomings include hard evidence of certification improperly bestowed, and severe limitations of go/no-go criteria (as with an automobile’s dashboard warning lights, we can learn if a performance trait is unsatisfactory — but a trivial excess produces the same indication as an imminent danger).

Necessary Changes

Extremely powerful and versatile means to improve performance have been available for a very long time. Kalman’s original paper, half a century ago, formalized an optimal way to achieve such performance. While Kalman estimation is commonly used today, its effective reach is almost invariably limited to data resident within each proprietary box of equipment.

The resources for providing centrally processed solutions for data from every source of information available, any combination of sources, any subset that may exclude any sensor or group, or any individual source in a federated configuration, are well known. Every conceivable choice from among these solutions can be made concurrently available; note the inherent backup.

However, all this capability is forsaken or lost by continued use of:
- interfaces chosen poorly or from outdated standards;
- undue consolidation within isolated equipment packaging;
- overextended proprietary rights; and
- limited, demonstrably flawed validation methods.
Drop Demands for Full Fix. An immediate explosion of benefits can follow from acceptance of partial information. Countless examples could be cited, but two obvious ones suffice:
- Within GPS or GNSS, not all space vehicles (SVs) would be simultaneously affected by scintillation; ionospheric disturbance effects vary with both location and time. A similar case holds for multipath. Data from some SVs could be rejected, by decisions made external to a receiver, without forcing rejection of all.
- Central processing — not within any one equipment box — has always offered potential for other sources (distance-measuring equipment or DME, and so on) to make up for incomplete sets of SV data.
My broad goal here is to take advantage of information not currently used and to prescribe corrective strategies. That objective has not been widely pursued due to perceived lack of urgency. GPS availability has thus far been more than satisfactory to a multitude of users — but that could change.

Availability Enhancements. For about two decades, the industry was effectively guided by a strong preference for the trait whereby every data refresh event was self-sufficient. A major reason for this was protection against gradual veering: a snapshot sequence is less sensitive than a continuously evolving path estimate. The cost, of course, is forfeit of benefits conferred by the sequence’s history. More recently, a middle ground was sought to mitigate the resulting loss; subfilters used as much new data as possible while making some use of knowledge from an estimator’s covariance matrix.

I promptly endorsed that approach and sought to carry it to the limit. A single-measurement receiver-autnomous integrity monitoring (RAIM) resulted, offering an independent integrity test for each separate observation. Despite its rigorous derivation, the technique is quite simple in practice. Further, it bridges a gap that formerly separated integrity test from optimal estimation, while also having significant advantages over conventional RAIM:
- separation translates to independence from other satellites, and therefore from geometry (effective DOP of unity)
- ability to use different error variances for different observations (for example, with nonuniformity in signal strength and/or elevation).
With this discussion, we have clearly left the realm of well-known subjects with self-evident prescriptions. Much of what follows likewise falls into the category of relatively obscure methods.

Beyond Position-Oriented. A time history
of GNSS observations, with or without an inertial measurement unit (IMU), inherently carries dynamic information. A file with observational history from multiple sources of course enables the aforementioned explosion of benefits. The obvious immediate offerings include:
- closing of data lapses via information sharing;
- intrinsic backup with automatic activation;
- vast reduction of latency effects (for example, from 200 meters to less than 1 meter at 400 knots after 1 second, with easily obtainable velocity accuracy below 1 meter/second);
- formation of 1-sigma projected future error (within reason).
Beyond these lie, once again, some lesser known techniques, including a few that are virtually nonexistent in operation at the time of this writing. With GNSS, the full potential of dynamics calls for a revisit of carrier phase.

Carrier-Phase Developments. Rather than pursuit of unnecessary sub-wavelength fixes for aircraft (for example, with 20-meter wing span moving at 400 knots), the true value of carrier phase in flight lies in enhanced dependability. Sequential changes in carrier phase over 1 second provide excellent dynamics information, with or without an IMU.

Recognition of this opportunity led to the concept of segmentation, whereby position is determined separately from dynamics. Carrier-phase sequential changes with ambiguities unresolved can provide precise (1-centimeter/second RMS with IMU; decimeter/second without) streaming velocity independent of position. Dead reckoning then provides a priori position correctible by pseudoranges.

One advantage of this scheme is subtle: with 1-second phase change propagation effects generally at 1 centimeter or less, no mask is needed. The geometry benefit is obvious, and flight experience has verified it. This raises another segmentation characteristic: the single-measurement integrity testing is applicable to each carrier-phase sequential change and to each pseudorange, separately and independently.

These capabilities are untapped in essentially all operational systems — air, land, and sea — and all stand to gain. Yet another opportunity can be added: ability to sustain operation even if every SV has repetitive data gaps. This advantage is best exploited with receivers described next.

FFT-Based Processing. Correlators and track loops in GNSS receivers can be replaced. The theory is age-old: multiplication in the frequency domain corresponds to convolution in time (and vice-versa). Thus a term-by-term product of a digitized receiver input’s fast Fourier transform (FFT) with the reference pattern’s FFT can, after an inverse FFT, provide outputs equivalent to full sets of correlator responses. Today’s processing and analog-to-digital converter capabilities offer feasibility.

In addition to reduced vulnerability to jamming (not covered here), advantages include:
- access to all cells (not only a track loop’s subset)
- guaranteed access (stability is not conditional)
- linear phase-versus-frequency; no phase distortion.
Features from the preceding section, combined with these traits, offer extreme robustness.

Extension to Surveillance. The practice of transmitting responses to RF interrogations has, for many decades, been quite vulnerable to overload (garble; one user’s information is everyone else’s interference). One report described the unsurprisingly poor performance during the first Gulf War, and identified a remedy: squitters with separate assigned time slots, spontaneously firing the transponder transmitter without interrogation. Immediately, a sea change in capability offers every participant an opportunity to track every other participant. With no interrogations, garble would disappear.

This dramatic increase in capacity has been successfully demonstrated with the use of an existing communication link and existing airborne equipment: GPS receivers and Mode S squitters. Subsequently I enthusiastically advocated adoption of the technique with one fundamental modification: replace the data bits of the transmitted messages with measurements instead of coordinates.
Additional improvements include small shifts in time (reducing bits needed for time tags) and recomputation of measurements that would have occurred at the center of gravity (to mitigate rotation effects). Collectively, the full set of procedures offers a vast and compelling list of benefits.

Conclusions

Capability and dependability of navigation and surveillance can be enormously increased. The key lies not in new inventions nor provisions, but in use of newer methods, (among them, FFT-based receivers, segmented estimation, and 1-second carrier-phase changes) while abandoning habits such as:
- dismissal of partial fix data
- preoccupation with full fixes for instantaneous position irrespective of dynamics
- preference for location pseudomeasurements rather than the measurements themselves
- reliance on proprietary software in equipment boxes
- RF interrogation/response sequences instead of squitters.
The industry can either adopt changes or continue to settle for performance levels at a minor fraction of the intrinsic capabilities available from our present and future systems.

James L. Farrell worked for 31 years at Westinghouse in design, simulation, and validation of navigation and tracking programs. He continues teaching and consulting for private industry, the Department of Defense, and university research through Vigil, Inc
December 1, 2009
GNSS Receiver Evaluation
Record-and-Playback Test Methods

This article addresses how best to quantify “which navigation system performs best” in a realistic testing scenario. The methodology focuses on land vehicles navigating in urban environments, but applies equally well to pedestrian navigation and can be adapted for testing assisted-GNSS implementations. During a drive test, the truth-reference system and RF recording system log samples to disk, with no need for the receivers under test to be included during the actual drive.

By Eric Vinande, Brian Weinstein, Tianxing Chu, and Dennis Akos, University of Colorado, Boulder

FIGURE 1. Traditional in-vehicle receiver testing.

Radio frequency record-and-playback systems (RPS) have recently become commercially available. These systems sample the RF environment and store it to disk during a drive test and can replay it through receivers back in the lab environment. Here we explore the improvements in dynamic testing methodology created by these units.

RPS test system installation.

RPS constitute a stark contrast to more traditional signal simulators that use pre-defined trajectories and mathematical models to determine appropriate RF output. Signal simulators attempt to reproduce environmental error factors such as multipath, inertial aiding system errors, and building and vehicle obstructions. They rely on mathematical models to simulate these various error sources. In some cases they do a reasonable job of reproducing these errors, but the dynamic urban environment is so complex (for example, rapidly varying/fading signal strength(s), multiple multipath signals, short/long duration obstructions of multiple layers) that even a sophisticated mathematical model can not replicate all effects completely. Some simulators include software that enables the user to define a trajectory and a limited amount of urban scenario details. Again, only so much realism can be created in a simulation environment. Existing testing standards are simulator-based, and as such, are circumscribed by the signal simulator limitations in representing a dynamic environment.

Positioning performance of a satellite navigation receiver under test (RUT) is coupled with its RF front-end system and local oscillator quality. Because of the variation in RF components between RUTs, some likely have superior RF interference (RFI) immunity. RFI can be a serious issue in certain land vehicles due to on-board electrical systems or because of external interference sources.

This article describes a testing method applicable to all receiver types, and complementary to that described in the December 2009 GPS World article by Mitelman and colleagues, “Testing Software Receivers,” regarding validation testing within a production environment. Added elements include taking into account truth-system uncertainty and a repeatability verification of the RF playback process through non-deterministic hardware receivers.

We present here the dynamic testing approach currently used at the University of Colorado in Boulder for receiver evaluation and comparison in the urban environment. The approach also includes the ability to assess the effect of sensor augmentations (for example, inertial, environmental) on positioning performance.

Truth Reference. Comparison with a truth reference system is essential for evaluation of satellite navigation receivers. For dynamic testing, this typically includes a survey-grade receiver coupled with a tactical-grade (or better) inertial measurement unit (IMU) and associated carrier-phase differential post-processing software. This software is filter-based and provides a positioning-error estimate in various components. Truth reference systems provide a continuous position estimate whose quality can vary depending on factors experienced in the urban environment, including length of full/partial satellite signal outage. In this study, we subtracted the 99th-percentile horizontal positioning error estimate of the truth system from the nominal RUT positioning error at each reporting epoch, as shown in Figure 2.

If the RUT position happens to lie within the truth-system position uncertainty, it is not considered to have any position error.

We focus here on a method to evaluate and compare mass-market, consumer-grade receivers to survey-grade receivers. One difference between these two receiver types is the way they handle the trade-off between accuracy and availability. Consumer receivers strive to provide the user with the highest availability, whereas survey receivers’ goal is to maximize accuracy. As a result, consumer-grade receivers will produce more regular position updates in harsh signal-tracking conditions, but must sacrifice accuracy to do so.

FIGURE 2. RUT position error calculation

Current Testing Standards

Currently accepted A-GPS standards such as those used by the 3rd Generation Partnership Project (3GPP) provide very limited dynamic testing in simulated urban conditions, being mainly designed to evaluate the first position calculation achieved in a particular simulated scenario. High-sensitivity receivers that pass or greatly exceed the 3GPP tests, in our opinion, are not guaranteed to have superior navigation performance in urban areas. Also, local oscillator performance is not specified. The trajectory dynamics imposed can actually be much smaller than the clock dynamics of a very low-cost local oscillator. A GPS receiver cannot tell the difference between the two and must track the effective Doppler variation.

The 3GPP defines five independent tests for A-GPS receiver certification. They include tests in the areas of: sensitivity with coarse/fine time assistance, nominal accuracy, dynamic range, multipath performance, and moving scenario/periodic update performance. The last three tests include elements that ostensibly pertain to the urban environment. These tests specify discrete, constant signal power levels for implementation in a hardware signal simulator. The discrepancy between the 3GPP-prescribed signal levels and those observed during actual drive testing is detailed as follows.

The 3GPP moving scenario/periodic update performance test trajectory is shown in Figure 3.

FIGURE 3. 3GPP dynamic testing
trajectory (van Diggelen, A-GPS: Assisted
GPS, GNSS, and SBAS, Artech House)

This test profile calls for the simulation of five satellites with a constant signal strength of 2130 dBm while the vehicle travels around the racetrack trajectory. In contrast, during an actual drive test in an urban area, a receiver reported the distribution of carrier-to-noise-density values for all tracked satellites as shown in Figure 4. This more accurately shows the range of signal strengths that should be expected in urban conditions.

FIGURE 4. Drive-test C/N₀ distribution

The 3GPP moving test is considered passed if positions are reported regularly, and 95 percent of them are within 100 meters of the true position. This is not a particularly difficult test for a RUT to retain signal lock through, as the linear acceleration is about 0.15 g and the centripetal acceleration is about 0.25 g.

It is difficult for independent third parties to carry out a receiver evaluation following 3GPP guidelines as several of the tests require receiver restarts, which in turn requires testing automation. Depending on the receiver-evaluation hardware availability, restart commands may not be available to to an independent evaluator.

3GPP receiver testing results are quoted as pass or fail over a large number of short evaluations. For the dynamic environment, the system performance over continuous time is required to make a proper comparison between evaluated receivers.

In general, evaluating the GPS engines embedded within cell phones or other devices is difficult. Most are not made to interface with an external antenna, and the mere act of adding an antenna connection can significantly alter performance. The output format is not always documented, if it is even available to an end user. To allow fair across-the-board comparisons, GPS chipset manufacturers should make available development kits that have external antenna connections and well-documented message output formats.

Drive-Test Configuration

Current live dynamic testing requires multiple systems to be operating in a moving vehicle (see opening Figure 1). A truth-reference system, usually a high-grade GPS/INS device along with post-processing, provides the basis to which all other RUT are compared. This system requires a dedicated vehicle rooftop antenna with the best possible sky view, separate from a lower-grade test antenna located within the vehicle. Each RUT is connected to the representative consumer-grade antenna located in the vehicle through a high-isolation splitter that suppresses inter-receiver interference. It is important at this point that the gain be set appropriately for each RUT, depending on the front-end expectations while maintaining an equivalent noise figure across all receivers.

Visualization Methods

In addition to quantitative methods, we have created a qualitative visualization to assist with interpretation of the raw data. The same parsed data sets that provide the statistical script input are fed into a viewer script along with the post-processed truth reference data. With the truth-reference system data plotted in the center of the screen, each RUT is then plotted the correct distance and direction away, based on the distance and direction of error compared to truth. The receiver plots are overlaid onto Google Earth images centered on the truth-reference location. Plots of number of satellites utilized (top right of Figure 5) and elevation (middle right) as reported by each receiver and the sampled RF spectrum (lower right) are also included.

For each reporting epoch, based on the data frequency of the truth-reference system, a frame is generated with the aforementioned characteristics. These frames are gathered and encoded into a movie clip which can then be used as a quick and simple qualitative tool for receiver comparison. Figure 5 shows an individual movie frame. A forward-looking camera capability is also being added to this movie so the test environment can be documented from multiple angles.

FIGURE 5. Movie visualization screenshot

While observing this movie, variations in the sampled RF spectrum from interference or blockages can be associated with the current landscape. Locations of RFI sources can be identified and avoided (or included) in future testing. These RFI and significant blockage locations are of interest for receiver RF component and navigation filter development. The next three figures show spectrum snapshots during various parts of a drive test. In Figure 6, the cumulative GPS spectra rises above the noise floor and is visible during open sky conditions. While below ground level, Figure 7 shows only the front-end filter shape (and relatively minor RFI). Figure 8 shows an example of severe RFI when near a specific parking garage location.

FIGURE 6. Open-sky spectrum (centered
on 1575.42 MHz)

FIGURE 7. Spectrum while below ground
level (centered on 1575.42 MHz).

FIGURE 8. Spectrum near interference
source (centered on 1575.42 MHz).

Record/Playback Concept

To overcome the limitations of hardware signal simulators and repeated vehicle drive testing, the RF record/playback testing method is utilized at the university. Commercially available equipment, capable of recording and playing back an RF signal, has recently become available. Equipment options exist for between $10,000–100,000, with 1–16 bit sampling and 4–25 MHz front-end bandwidth.

Figures 9 and 10 show the concept of “record once, playback many times.” During a drive test, the truth-reference system and RF recording system log samples to disk. There is no need for the RUT to be included during the actual drive test.

FIGURE 9. Recording mode block diagram.

FIGURE 10. Playback
mode block diagram

In the laboratory, the logged RF samples are replayed through a splitter to all RUT. The effect of receiver configuration changes can be evaluated without having to repeat the drive test. At a later time, additional receivers can also be tested using the same stored RF sample file.

During separate record and playback phases, testing considerations and methods discussed previously are implemented.

Since the recording process can only obviously capture current conditions, additional drive-test collections are required if different satellite geometry is desired, or if additional representative antennas need to be evaluated.

Repeatability of RPS Testing

To validate that the playback signal levels were not significantly different from live signals, we conducted an urban, dynamic evaluation. Figure 11 shows that there is typically not more than a 1 dB difference in reported C/N₀ between live and playback modes when testing a receiver that only reported integer values. The two dropout instances were excursions into parking garages.

FIGURE 11. Live and playback C/N₀ values

Figure 12 compares the navigation statistics between replays, using the same five playbacks as in Figure 11. The playbacks show a 1-sigma horizontal position solution spread under 1 meter for approximately 83 percent of the test.

FIGURE 12. Playback Horizontal Position Error Spread.

These two figures verify the repeatability of the RPS testing method and solidify it as an alternative to both signal-simulator testing and live testing of satellite navigation receivers.

Denver Testing Method

To evaluate the RPS concept, we conducted tests in three locations: Boulder, Denver, and Interstate Highway 70, all in Colorado. The Boulder and Denver locations were urban collections, while the Interstate 70 location was a natural canyon with significant elevation change. The collection at each location was repeated with two different representative antennas (patch and cell phone) at nearly the same sidereal time in order to keep the overhead satellite constellation similar.

We examine here the November 11 and 16 Denver tests. The November 11 test used a patch antenna that places nearly all its gain in the upward direction, making it more immune to interfering sources below and to its sides. Figure 13 shows the patch antenn
a location on the van, as well as the truth-system antenna location utilized for testing on both days.

FIGURE 13. Patch antenna (dashboard) and
truth-system antenna (rooftop) locations.

The November 16 test used a cell-phone GPS antenna that does not have a preferential gain direction, making it more susceptible to interfering sources below and to its sides. This antenna type is representative of the typical low-cost antenna (in some cases as simple as a piece of wire) found in consumer cell phones. Figure 14 shows the cell-phone antenna suction-cup mounted to the front window of the testing van. The representative antenna mounting location was chosen to minimize locally-generated RFI effects while also being representative of a typical vehicle-use case.

FIGURE 14. Cell-phone antenna location.

The required equipment and connections are minimal when performing RPS drive testing, as no RUTs are included. The inset to Figure 1 at the beginning of this article shows the RPS unit in the rear of the van, mounted on layers of foam to reduce vibration, which, if not properly addressed, can cause errors in mechanical hard drives writing data at high rates. Also visible are the truth receiver on the center of the van floor, and the car batteries for powering it and the IMU. The IMU is mounted to the vehicle frame and is not shown.

The test drive trajectory through Denver on November 11 and 16 as reported by the truth system is shown in black in Figure 15 and is also repeated in Figures 16 and 17. The test lasted approximately 40 minutes on both days. It started in the upper left part of Figure 15 and continued zig-zagging through downtown to the lower right.

FIGURE 15. Truth trajectory for November 11 and 16 tests.

Figures 16 and 17 show particularly difficult blocks for the four receivers tested under the replay method. These receivers are denoted A (green), B (blue), C (red), and D (yellow).

FIGURE 16. Difficult block #1 during November 11 test and truth
system antenna (rooftop) locations.

The horizontal positioning error statistics for two receivers on the November 11 test are shown in Figures 18 and 19. The left side shows horizontal error in two different zoom levels. The right side shows a histogram and cumulative distribution of errors, and several reporting metrics over the entire test. Even though receiver A in general outperformed receiver B, from the error time histories there are noticeable periods where both receivers simultaneously had positioning difficulties.

FIGURE 17. Difficult block #2 during November 11 test.

Table 1 summarizes the horizontal positioning statistics for all receivers during both tests. Positioning accuracy was severely degraded when replaying samples collected with the cell-phone antenna as compared to the patch antenna. Receiver A was the most accurate across both tests, while receiver B was the least accurate. The uncertainty of the truth system was subtracted out when producing the horizontal positioning results for all receivers.

Table 1

Conclusions

The record-and-playback system testing approach, in our opinion, represents the best way to test hardware receivers. It overcomes the fidelity limits of simulator-based testing, especially when considering the difficult-to-model urban environment. During receiver development, it requires only a single drive test for each location, as sampled RF data can be replayed from disk.

FIGURE 18. Receiver A horizontal positioning error statistics (November 11 test).

FIGURE 19. Receiver B horizontal positioning error statistics (November 11 test).

Having demonstrated that RPS testing is repeatable, we have produced a library of RF sample files representing real-world conditions for continued receiver development and testing purposes.
- Eric Vinande is Ph.D. student at the University of Colorado studying GPS/MEMS inertial sensor integration and urban RFI aspects.
- Brian Weinstein is a BSEE student participating in the Undergraduate Research Opportunity Program for GNSS receiver testing at the University of Colorado.
- Tianxing Chu is a visiting researcher at the University of Colorado from Peking University where he is a Ph.D. student.
- Dennis Akos is an associate professor within the Aerospace Engineering Sciences Department at the University of Colorado with concurrent appointments at Stanford University and Luleå University of Technology.
Manufacturers

Development of the methodology described here used two different RPS systems, one from LabSat (RaceLogic) and one from Averna. The test data come from the Averna system.
January 1, 2009
Oxford Technical Releases 6-Axis Inertial+

Oxford Technical Solutions (OxTS) has rolled out its Inertial+, an inertial navigation system that can be used with an existing GPS receiver to improve position measurements, according to the company.

The Inertial+ includes inertial sensors, processing engine, and algorithms. It is built around a 6-axis inertial measurement unit — including three angular rate sensors (gyros) and three servo-grade accelerometers — to measure position and velocity even when GPS is not available, OxTS said.

Designed for surveying in an urban environment, the Inertial+ is able to ignore or correct jumps in the GPS measurements. In addition to position data when combined with a GPS receiver, the device will also produce measurements like roll, pitch, and heading. Data is read and output in NMEA format, and other formats are supported. By combining an Inertial+ with a high-accuracy real-time kinematic (RTK) GPS receiver, users can achieve 1 centimeter precision, 0.03 degrees roll/pitch accuracy, and low drift rates when GPS is not available, according to the company.

April 30, 2008
U.S. DOT Plans to Continue Inland NDGPS Ops — for Now

The U.S. Department of Transportation (DOT) has approved a decision to continue the inland component of the Nationwide Differential GPS (NDGPS), based on the results of a user assessment conducted by the Research and Innovative Technology Administration (RITA).

RITA assessed the current user needs and systems requirements for the inland component of NDGPS. It gathered information through public responses to a notice in the Federal Register (including responses from state and local governments, the private sector, and the non-profit sector), and through quantification of the mission requirements of other federal agencies using inland NDGPS, according to DOT.

But this doesn’t mean that funding of inland NDGPS is not still up in the air. Earlier this year, DOT included in its fiscal 2009 federal budget request a $4.6 million line item in the RITA budget for NDGPS operations and maintenance of the current system through October 2009.

Discussions are ongoing regarding the program’s future funding mechanism, and will be addressed in future budget submissions, DOT said. The National Space-Based Positioning, Navigation and Timing Executive Committee endorsed DOT’s decision at its meeting in March.

April 30, 2008
Expert Advice: NDGPS Cut-Off Premature

By Charles R. Trimble

As we look forward in the modernization of GPS, and we’re looking at the spectrum of other systems that are coming online, GPS today has fundamentally the preeminent position in terms of positioning and navigation. If we don’t shoot ourselves in the foot in the transition from the GPS we have today to GPS III, which is 10 years out, GPS will probably remain the fundamental standard, because the only way non-military uses of these additional systems will get early use is by receiver manufacturers putting in dual-reception capability and using the new satellites as they go up, fundamentally as additional ranging signal augmentations. It’s the only way you get early use out of getting a few satellites in the sky.

A lot of whether GPS will retain its standard position has to do with worldwide confidence in the system. We’ve done a pretty good job of maintaining a level playing field for everyone in the world with regard to GPS. There haven’t been the problems that were experienced with Loran systems which were occasionally turned off, creating consternation in Europe. But the possibility, currently under consideration, of actually dropping an important accuracy augmentation element of GPS — the Nationwide Differential GPS (NDGPS) — before alternatives are available would certainly undermine worldwide confidence in the U.S. commitment to continuing to provide service equal to or better than what is already there.

The key issue here: You can have all the paper designs in the world you want, but fundamentally the question is once you have a given level of capability, how well is that maintained — and is it improved over time?

With all the machinations that have gone on, the United States has done a pretty good job. It basically delivers a set of signals that are better than promised. The system, especially with its augmentations, is clearly better today than it was 10 years ago.

Now, the U.S. from a policy standpoint does need to transition from where we are to GPS III. We simply need to do it in a wise manner. The problem that I see with zeroing out the budget for NDGPS is that we save very little money — about $10 million a year to maintain the system. For any accountancy firm, this would fall below the line of relevance in the budget. And the effect, in undermining international confidence in GPS and in direct costs to state and local governments, would far outweigh any such savings.

Until we have something in GPS III that provides accuracies in the half-meter range, which is what’s required for civil Geographic Information Systems (GIS) work, it would be foolish to turn NDGPS off. We would be degrading a system without any real alternative.

Furthermore, you’re probably going to cost state and local and federal governments, who use NDGPS extensively for local mapping, far more than $10 million by turning the system off.

I believe the main commercial use of NDGPS, outside of the GIS realm, is precision agriculture. The arguments to put it in originally were to provide the people on the interior of our continent the same sort of services that the coastal regions are provided. The issue we have is we don’t have a strong vocal constituency, and frankly state and local governments can’t provide much of a hue and cry for degradation of service.

And losing confidence, undermining international confidence in the U.S. to maintain a stable system, is not a party to the table, either.

Granted, international users do not actually use NDGPS itself. But they have invested the money to put in comparable base stations in their countries. For the U.S. to discontinue NDGPS undermines and brings into question whether their investment was a good investment — and whether, as an international user, you can comfortable continue to rely on GPS.

It’s a confidence issue. There is no economic damage to foreign users. But it’s a perception of undermining GPS credibility across the globe if we pull back support from a system that just a few years ago we deemed to be important and almost essential.

Some precision ag and other potential NDGPS users have switched over to WAAS, the Wide Area Augmentation System. There’s no question that WAAS is a good system, but you’re not going to get below a couple of meters, and you’re certainly not going to be able to farm above buried water tape. There’s clearly a market and I believe it’s part of the mix. It turns out it’s really tough to get at the 20-centimeter accuracy level over large distances, and WAAS will not give you that.

At some point in our transition — I don’t know whether it’s five years from now or 10 years from now — the world is going to be a different place in terms of satellite services and the level of satellite services. It may very well be at some point in the future, this space of 20–50 centimeter accuracy can be very well delivered by a private service (without interference in the RF spectrum), or let’s just say, can be delivered by satellite.

At that time, when there are truly other alternatives, I’m not going to be beating my shoe on the desk to maintain a legacy system. The issue in this whole positioning and navigation field is that as people are starting to get economic value out of information, introducing hiccoughs into the user stream of productivity enhancement is not a good thing.

We say that until there is a viable alternative for the 20–50 centimeter space, we ought to continue sending out the signals. Once there is a viable alternative, then you can certainly transition; look at the cost of transition, and you will probably transition.

But it turns out this is a relatively cheap way of providing information in this space and, frankly, we’re a long ways away from using GPS in automated systems that are directly related to safety of life. To get that, you have to play the game that the FAA plays, and worry about seven nines of reliability [99.9999999 percent]. GPS in its augmentation is probably at the one to two nine level. But as the usage increases, by having multiple augmentation systems and using them, there is no reason that reliability can’t be increased.

Fundamentally, the word to government is it’s premature to shut off the lights. It may be the right decision at some point in the future, but I think it would cause a lot more problems than the $10 million it would save if it’s done now.

CHARLES R. TRIMBLE is chairman of the U.S. GPS Industry Council.

October 1, 2007