Auto Safety

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GPS-Based Relative Positioning Test Platform

for Automotive Active Safety Systems

Chaminda Basnayake, C. Christopher Kellum

General Motors Research and Development

James Sinko, Joseph Strus

SRI International

BIOGRAPHY

Chaminda Basnayake is a Senior Research Engineer at

General Motors Research and Development Center. He

holds a Ph.D. in Geomatics Engineering from the

University of Calgary, Canada, and is a former Research

Associate in the University of Calgary Position, Location

and Navigation group. Dr. Basnayake is currently leading

the GNSS-based vehicle navigation technology researchand development efforts at GM R&D including vehicle-

to-vehicle communication-enabled relative positioning.

C. Christopher Kellum is a Project Engineer at General

Motors Europe Advanced Engineering. He holds a B.Sc.

in Electrical Engineering from Kettering University, and

an M.S. in Electrical Engineering Control Systems from

the University of Michigan. Since 2001, he has worked in

North America and Europe on collision avoidance

systems for automobiles, with emphasis on the use of

digital maps and vehicle-to-vehicle communication.

James W. Sinko is a Principal Engineer at SRIInternational. He holds a B.S. in Engineering Science and

M.S.E.E. from Stanford University, and a Ph.D. in

Electrical Engineering from the University of Rochester.

Dr. Sinko has been with SRI since 1967, working with

radar and aircraft systems. For the last 11 years, he has

been working with precision GPS for military and civil

applications.

Joseph M. Strus is a Systems Analyst at SRI International,

where he has worked on precision navigation applications

since 2001. Previously, he was a GPS Systems Engineer

with the Government Systems Division of Rockwell

Collins. Dr. Strus holds a B.S. and Ph.D. in Mathematicsfrom the University of Illinois at Urbana-Champaign.

ABSTRACT

Driver Assistance Systems (DAS) are designed to aid

drivers with normal driving scenarios in day-to-day

driving. This paper discusses the results of a recent GPS

relative positioning test platform development effort by

General Motors Research and Development for

automotive DAS. This GPS-based system does not

require additional GPS infrastructure and is intended for

use with positioning and communication systems built

into vehicles.

The test platform was used to evaluate several grades of

GPS receivers, with the objective of identifying hardware

that can deliver the accuracy and latency required fordifferent DAS applications, while fulfilling the cost

constraints of mass deployment. The test system is

implemented in several cars equipped with 802.11-based

communication capability. The vehicles broadcast

messages containing GPS and other vehicle data at

regular intervals. Each vehicle DAS uses these broadcasts

from neighboring vehicles to aid drivers with different

functionalities, ranging from warnings to automated

evasive action when the driver did not respond to an

imminent collision.

The GPS test platform consists of two modules: Receiver

Manager (RM) and GPS Solution Generator (GSG). TheRM acts as the interface between all GPS receivers and

the GSG and contains GPS relative positioning

algorithms. Two high-end receivers are installed on each

vehicle to provide a fixed baseline truth reference, which

greatly increases reliability of the single-epoch Real-Time

Kinematic (RTK) solutions generated by the GSG. The

relative positioning solution for low-cost receivers uses a

Kalman filter approach to estimate the float ambiguities,

which yields around 0.5 meter accuracy. This accuracy is

sufficient for lane-level applications.

System performance was tested in environments ranging

from freeways with open skies to heavily foliatedsuburban areas. System availability ranging from 80–99%

was observed in most test routes, with least availability in

heavily foliated environments. In open sky routes, the

low-cost system produced relative horizontal position

accuracy of better than 0.25 meters 98% of the time. On

more obscured routes, 96% of verifiable measurements

were within 0.5 meters of the truth system, limited by the

time duration when the truth system was able to provide a

solution.

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ION GNSS 19th International Technical Meeting of the

Satellite Division, 26-29 September 2006, Fort Worth, TX



INTRODUCTION

DAS are designed to aid drivers during normal driving

scenarios and are expected to help drivers avoid collisions

altogether, thereby making automotive transportation

safer. DAS aid drivers in different ways, from providing

warnings to taking automated evasive action when a

driver does not respond to an imminent collision. The

advent of low-cost, large-bandwidth vehicle-to-vehicle(V2V) communication systems and constantly improving

Global Navigation Satellite Systems (GNSS) technology

has offered another technology alternative for DAS over

conventional in-vehicle sensors such as radar and vision-

based sensors. The automotive industry is currently

evaluating the benefits of V2V and GNSS as a

replacement or add-on sensor system for conventional

sensors.

Communication-Based Safety Applications

Object detection sensors, such as radar or vision systems,allow a vehicle to indirectly obtain information about

other vehicles within a certain vicinity. V2V

communication, however, allows a vehicle to share

information directly with surrounding vehicles. Aside

from position information, the information shared by V2V

communication is more accurate and consists of a larger

set than that detected by an object detection sensor. For

example, an object detection sensor must estimate

acceleration of another vehicle by differencing sequential

measurements of relative speed, or by double-differencing

measurements of relative position. Using V2V

communication, however, a vehicle can immediately

broadcast its actual acceleration from information

maintained by its own vehicle safety systems, such as

Electronic Stability Control [Ghoneim 2005].

Information shared using V2V enables existing

applications, such as Adaptive Cruise Control, to operate

more effectively, since information such as speed and

acceleration is more accurate than that measured by object

detection sensors. However, the real benefit of V2V

communication is the set of new applications enabled by

the technology. These new applications emerge from a

combination of the omni-directional V2V communication

capability and the unmatched positioning capability of

GPS. These two technologies allow a vehicle to

efficiently share state information, as well as otherdriving-related information, on a constant or on-demand

basis. Other information shared between vehicles may

include safety-related information, such as debris on

roadway, or even entertainment-related information, such

as shared audio or video.

The capabilities of Dedicated Short Range

Communications (DSRC) technology and GNSS

positioning technology as applicable to V2V DAS

applications has been investigated extensively in previous

research projects by General Motors, as well as by

consortiums of automotive manufactures. Tests conducted

by Crash Avoidance Metrics Partnership (CAMP) Vehicle

Safety Communications Consortium have shown that

communication between vehicles using DSRC is reliable

within a range of approximately 300 meters in each

direction along a roadway (CAMP Task 4 2003). Undermany real-world conditions, testing at General Motors has

shown that communication continues for up to 700 meters

in each direction along roadway; however, the percentage

of information lost is higher.

In terms of GNSS technology, accuracy and availability

are key performance requirements for V2V DAS. The

implementation outlined in this paper and used in

demonstrations conducted by General Motors has shown

meter-level V2V positioning capability with low-cost

GPS receivers [Duffy, 2005]. In terms of availability,

more than 100,000 miles of driving data from the

Automotive Collision Avoidance System FieldOperational Test has shown that GPS outages of 5

seconds or longer account for less than 0.5% of all driving

distance [GM and Delphi-Delco 1998]. Although this may

not represent cases where the majority of driving is in

challenging environments for GNSS, augmentation

technologies are expected to develop such that at least a

certain subset of V2V DAS are operational using

alternative sensors and intelligent integration

mechanisms.

V2V communication and GNSS enable a host of DAS

applications, such as Intersection Collision Avoidance,

Forward Collision Warning, and Blind Spot Warning,

which will improve driving safety for equipped vehicles.

These three applications require tracking neighboring

vehicles, and assessing the threat each vehicle poses to the

host vehicle using V2V messaging and GNSS-based

positioning. As the threat level rises, the vehicle takes

action to inform the driver of the threat, with the goal of

avoiding the crash or reducing crash severity. The

technology also enables applications where vehicles share

information that has been learned. For instance, a vehicle

can estimate road friction from an antilock brake or

stability control system event and inform other vehicles of

the road friction in a specific area. Similarly, vehicles can

share road grade information to enable better powertrain

controls, and share traffic congestion information to helpdrivers avoid congested areas. Many such applications

have been identified by the CAMP consortium and are

described in detail in [CAMP Task 3 2003].

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GM Active Safety Demo Vehicle Fleet

General Motors recently unveiled a fleet of vehicles

equipped with DSRC V2V communication and GPS

positioning capability. Different DAS features enabled in

these V2V demo vehicles are used for test and validation,

as well as for DAS demonstration purposes. Some of

these DAS applications were publicly demonstrated at the

2005 12th World Congress of Intelligent TransportSystems [Duffy, 2005]. Figure 1 depicts three V2V demo

vehicles in a V2V DAS demonstration run.

Figure 1. General Motors V2V demo vehicle fleet.

In addition to communication and positioning capability,

GM V2V demo vehicles are equipped with various

displays and actuators. These include High Head-Down

Displays (HHDDs), mirror icons, haptic seats, and certain

brake controls. The indicators are mostly application

specific and some, such as the HHDD, are used in

multiple applications. Figure 2 shows the mirror icon

indicating the presence of a vehicle in the blind spot of

the host vehicle (left), and the HHDD icon indicating the

presence, caution, and warning (green, amber, and red,respectively) generated by a vehicle in front of the host

(right).

Figure 2. Side mirror blind-spot warning (left), and

HHDD forward collision warning status (right).

Altogether, four different DAS features are implementedin V2V demo vehicles; namely, Slow/Stopped Vehicle

Ahead Advisor (SVA), Emergency Electronic Brake

Light (EEBL), Forward Collision Warning (FCW), and

Lane Change Advisor (LCA).

The Slow/Stopped Vehicle Ahead feature identifies

vehicles that are stopped or moving slowly on the

roadway ahead of the host vehicle. When this occurs, a

stopped vehicle icon is illuminated in the HHDD to notify

the driver. The purpose of this feature is to provide

potentially useful information in a non-intrusive manner.

The Emergency Electronic Brake Light feature identifies

events in which a vehicle ahead of the host is braking

hard. This feature extends the driver’s view by providing

important information that cannot be seen by the driver

due to the presence of other vehicles between the brakingvehicle and the host. When the system detects an unusual

braking event ahead on the same roadway, an audio tone

is triggered along with a visual display to attract driver

attention to the potentially dangerous situation ahead.

Hence, this feature allows the driver to react faster and

decelerate safely.

The Forward Collision Warning feature identifies vehicles

in the same lane ahead of the host vehicle, and attempts to

recognize scenarios where the host vehicle might rear-end

another vehicle. Relative distance, relative velocity, and

relative acceleration are important to properly identify

rear-end crash scenarios. When the system detects a rear-end crash scenario, a visual display, an audio tone, and

the forward-most portion of a haptic seat actuate to attract

driver attention to the roadway ahead.

The Lane Change Advisor feature monitors traffic in

lanes adjacent to the host vehicle, and displays

information to the driver when a communicating vehicle

is detected in the host vehicle blind spot. This feature also

alerts the driver when a lane change maneuver is unsafe

due to a passing vehicle equipped with the technology.

The intention of this feature is to notify the driver when

there are vehicles in adjacent lanes that the driver may

have difficulty seeing. A display embedded in the side

mirror illuminates an icon to warn the driver of the

presence of a vehicle in the host vehicle blind spot.

Positioning Needs of Active Safety Applications

While relative positioning and velocity estimation

accuracy is critical for these safety applications,

reliability, availability, and computational efficiency are

also of great importance. These performance factors are

derivations of positioning and communication

performance. In terms of availability, General Motors has

several research and development initiatives on

integration of in-vehicle sensors to augment GNSS. Theseaugmentations are expected to improve availability,

particularly in urban environments. However, this paper

investigates only the GNSS accuracy, reliability,

availability, and computational efficiency of the V2V

positioning platform.

System accuracy requirements depend on the safety

feature. Slow/Stopped Vehicle Ahead and Emergency

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Electronic Brake Light features require the system to

distinguish which vehicles are on the same roadway as the

host vehicle, and do not require a distinction on the lane

configuration of the vehicles. This requirement is defined

as road-level accuracy and is estimated as 5 meters.

Forward Collision Warning and Lane Change Advisor

features require the system to distinguish between in-lane

and out-of-lane vehicles, with respect to the host vehicle.In the case of Forward Collision Warning, the system is

primarily interested in vehicles in the same lane, while

Lane Change Advisor uses information regarding vehicles

in adjacent lanes. The system requirement to distinguish

relative lane position of other vehicles roughly translates

into a relative positioning accuracy requirement of a

meter or better, and is defined as lane-level accuracy.

The analysis presented in this paper includes achievable

accuracy and availability using low-cost GPS devices and

GPS estimation-related latency performance using the test

platform. Low-cost device accuracy was derived using the

high-end GPS reference system built into the testplatform.

RELATIVE POSITIONING TEST PLATFORM

The relative positioning test platform was developed to

enable one of two vehicles to generate the near-real-time

relative position and velocity of both vehicles. The

purpose of the test platform was three-fold. First, the

system must provide a standard interface for the simple

transition of multiple receiver types to enable the

performance evaluation of different low-cost GPS/GNSS

hardware. Second, the system should include a reference

system that functions as a truth system to validate low-

cost alternatives. Third, the system should include

advanced algorithms for optimal accuracy, availability,

and reliability performance from low-cost receivers.

Combined solution latency was not considered crucial,

and was required to be less than 250 milliseconds.

Major components of the instrumentation include the GPS

hardware (high-quality reference and low-cost

alternative), a relative positioning computer, and a

wireless bridge built on DSRC technology. The high-

quality reference receiver was chosen for its fast recovery

of carrier phase and good multipath rejection. The low-

cost receiver was chosen for its ability to outputmeasurements close to (within a microsecond) the GPS

second. For test purposes, the system was implemented on

only two vehicles, one serving as the reference and the

other as the rover . However, the software was

modularized with the ability to expand into multiple

vehicles such that each vehicle can function as a reference

as well as a rover.

System Hardware Configuration

The system configuration, with one vehicle instrumented

as a reference and the other instrumented as a rover, is

illustrated in Figure 3. Both vehicles were instrumented

with low-cost evaluation receivers (L1 only) and

reference generator high-end receivers (L1/L2). The

reference vehicle was equipped with an additional

reference receiver to provide the redundancy needed forrobust reference information generation. A radio link

between the reference and rover vehicles was developed

based on 802.11b (2.4 GHz). The communications link

consisted of a wireless bridge router in each vehicle,

along with supporting antennas and an Ethernet-based

interface to the position computer. Data from the

reference vehicle was sent to the rover to generate relative

position solutions.

Figure 3. Test platform system configuration.

System Software Configuration

The test platform software was composed of two

components: the Receiver Manager (RM) and the GPS

Solution Generator (GSG). Either component can

function in standalone mode or in RM–GSG combined

mode.

RM Overview

The RM was tasked with configuring GPS hardware on

startup, recording GPS data to files as necessary (user

configurable), and distributing GPS data to GSGs.

Operation of the RM was controlled by an ASCII

configuration file that can be used to configure hardware

connection, data logging, and data distribution functions.

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The RM was capable of recording raw receiver data in

multiple formats to facilitate post-processing.

The RM distributed data from the receivers/wireless link

via socket interfaces and acted as a server. The RM

allowed multiple clients to connect, so that multiple GSGs

were able to obtain data from GPS receivers

simultaneously. Control parameters for the RM included

the following:

• Configuration information for reference and low-

cost receivers

• Data logging requests and format information

• Port information of requests to GPS data

connections

• Serial connection information for receivers

GSG Overview

The GSG received data from two RMs by connecting to

an RM as a client. GSG is designed such that, in full-scaleimplementation, it will be able to connect to RMs in all

equipped neighboring vehicles. GSG generates near-real-

time relative position and velocity estimates from the GPS

data. GSG algorithms are capable of operating in single-

frequency or dual-frequency mode and are user

configurable. GSG estimates are transmitted via a socket

interface to user computers. If one of the RMs supplies

additional high-accuracy truth data, the GSG is able to

provide metrics for accuracy. Configuration of the GSG

includes the following:

• IP numbers and port numbers for connecting

RMs• Local RM (only the local can process truth data)

• Local RM reference data availability

• Single- or dual-frequency data availability

• Logging information for GSG estimates

Recorded data from all connected RMs and GSGs can be

processed to obtain the same type of output as the near-

real-time system. The post-processed low-cost data can be

used to evaluate the performance of the low-cost GPS

hardware.

Reference Solution Generation

One of the main problems with RTK GPS is the

validation of RTK estimates with a high degree of

reliability. Low residuals and a low value of the ratio of

the residuals of the best solution to the second best

solution are widely used for such validations [Misra and

Enge, 2001]. However, these tests discard many good

solutions.

Using RTK GPS, once solution confidence has reached a

certain threshold, an additional check is made to verify

that the integers do not change for satellites common to

previous epochs. This method of error checking performs

rather reliably under normal driving scenarios—until a

tracking failure is encountered due to obstructions such as

an underpass or tree cover. In such case, the same

confidence-building procedure must be repeated, as the

tracking-related information set has changed.

The error checking method adopted for the L1/L2 truth

solution in this test platform used two separate antennas

and high-end receivers, and checked to see if the distance

between them as estimated by RTK GPS was consistent

with the physical distance between them. The RTK

solution for each receiver was estimated, and based on

these solutions, the distance between the two antennas

was calculated. If the calculated distance-between-

antennas significantly differed from the known distance

compared to a predefined threshold, both solutions were

rejected. Additionally, the elevation angle was checked to

ensure that it was a reasonable value.

Figure 4 illustrates the reasoning for using a two-

antenna/two-receiver setup rather than using the ratio test

to determine the correct estimates. The driving route for

this data was through suburban and commercial areas

ranging from fairly clear to heavily canopied with trees,

with a short segment of freeway driving. Overall, 80% of

epochs had four or more satellites in lock. Figure 4

depicts the cumulative probability for the ratio for the

epochs determined to be correct (accepted) by the two-

antenna system, as well as the cumulative probability

distribution for those determined to be incorrect

(rejected). If a ratio test was used at a typical 0.3 value,

2.1% of epochs would be in error. More importantly, only

29% of the correct solutions would have been accepted.

Using the Ambiguity Dilution of Precision (ADOP) at a

level of 11, 2% of epochs would be in error, and a slightly

better 36% of the correct epochs would be accepted. With

the two-antenna test, 81% of solutions were accepted as

correct.

It must be noted that, for these tests, obscuration was a

much more serious problem than it would be for a single

vehicle: both vehicles must simultaneously have an

adequate number of GPS satellites visible. We have not

yet analyzed the gap bridging time requirements for the

case in which both vehicles determine their position usingan external reference station.

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Figure 4. Cumulative probability of ratio values for

accepted and rejected epochs.

Data Processing and GSG Capabilities

Several solution types were considered for the GSG. The

following review outlines the considerations involved in

designing the final GSG solution procedure.

A single-frequency, single-epoch least-squares relative

solution was considered. In principle, with sufficient time

in lock, such a solution would provide sub-meter

horizontal accuracy. However, part of the design goal was

to provide a solution capable of decimeter-level relative

positioning accuracy, and it seemed that least-squares

relative solutions would not be capable of fulfilling that

requirement. Nonetheless, straight-forward Hatch filtering

[Misra and Enge 2001] with carefully chosen dynamic

weighting and a single-epoch least squares solution

should not be discounted for such applications.

SRI has considerable experience in dual-frequency single-

epoch fixed integer solutions, and an attempt was made to

use similar methodology in the single-frequency case

[Sinko and Strus 2002 and Sinko 2003]. However, single-

frequency ambiguity resolution is much less reliable than

its dual-frequency counterpart. As such, single-frequency

applications are typically used only over multiple epochs

and on short and known baselines, such as in attitude

determination systems. However, an attempt was made to

provide unconstrained, single-frequency, single-epoch

solutions for this project. This technique produced usable

solutions for less than half the total number of epochs,

even under ideal conditions. As such, this type of solutionproved too risky for automotive safety applications.

Another solution was considered with the inclusion of

vehicle dynamics. Since the vehicle type is known, it was

assumed that the relative dynamics could be modeled.

Therefore, it made sense to incorporate vehicle states into

a filter and recursively estimate float ambiguities. Similar

attempts were found in research literature, and

considerable room for improvement was noticed in

reported results [Ford et al. 1994]. Several commercial

packages were evaluated as a preliminary study, and it

was noted that they did not perform very well, especially

in foliated environments.

The final design for the GSG solution is a Kalman filter

based solution. The filter uses three state variables for the

inter-receiver baseline, three states for the baseline rate-

of-change, and one state for each single-frequency doubledifference. Extensive testing was done to estimate both

the process and measurement noise. In particular,

exceptional care was given to multipath estimation in both

stationary and dynamic environments. Process noise was

estimated as a function of the current dynamics. The

design also provided for a solution based on fixed

integers. The integer fixing is based on Lambda technique

and is discussed in detail in Sinko [2003]. However, the

majority of effort was based on a float solution.

GSG PERFORMANCE EVALUATION

Performance evaluation involved initial bench testing and

validation of the system to low-cost GPS evaluation tests

in different test environments.

Initial Testing / Filter Tuning

Bench testing consisted of stationary testing to debug

communication issues. Timing routines were

implemented to estimate latency in the system. It was

found that a fully loaded GSG (one that computes one L1

filtered solution and two L1/L2 fixed solutions) runs in

about 100 milliseconds, approximately half the time used

by Lambda algorithms.

Initial dynamic tests began with the rover and reference

configured on the same vehicle, with a known antenna

separation. With a fixed, known relative distance,

preliminary filter tuning was simplified. This filter tuning

phase was conducted only with the high-cost receiver. As

an example scenario, the test vehicle was driven into an

open area near the San Francisco Bay. The test area

provided open sky access down to an elevation angle of 5

degrees. The system was powered up as the vehicle was

driven at coasting speeds. In post-processing, the true

inter-antenna distance was subtracted from the estimated

distance calculated by the GSG. The results were used toestimate filter uncertainty parameters.

Figure 5 indicates the true error and the filter standard

deviation. In general, the filter is tuned pessimistically by

design. The initial standard deviation depends upon the

exact constellation at the time. However, the figure results

are fairly typical. In this case, the filter range standard

deviation starts at about 2.3 meters and converges to 0.5

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meters in 60 seconds. The oscillations in the solution were

found to be correlated with vehicle turns, and were

attributed to errors such as correlated multipath. This

becomes less of an issue in separate vehicles.

Figure 5. True range error (blue) and filter uncertainty of range error (red).

Menlo Park Tests

First full system testing was done on a route with a wide

variety of coverage and speed conditions. The Menlo Park

route started from the SRI facility on Ravenswood

Avenue and continued onto El Camino Real (a

commercial district), Sand Hill Road (heavily foliated), I-

280 (a reasonably open freeway), Farmhill Road

(moderately foliated) and the Canada College Parking Lot

(open sky). The high-cost receivers with truth verification

were run in real-time. The low-cost receiver data wasrecorded in real-time, and the solutions were post-

processed.

Figure 6 depicts range errors for both the low-cost

receiver solution and the high-cost receiver solution. Note

that low-cost receiver has some poor-quality solutions

under heavy canopy, but under open sky conditions at

highway speeds, the solution appears to be within the

1 meter error bounds. The high-cost receiver has good

solutions throughout, but there is a slight section of

outliers under heavy foliage. Table 1 presents the

statistics for the entire run. Results in Table 1 also

indicate that the algorithms meet our accuracy goals, evenin heavily foliated environments.

Figure 6. Range error for low-cost receiver (red) and

high-cost receiver (blue).

Integer Fixing

In addition to the single-frequency filter solution, the

fixed integer solution for the high-cost receiver was

computed, as illustrated in Figure 7.

Figure 7. Range error for filtered solution high-cost

receiver (blue) and filtered and fixed solution (red).

In obtaining the fixed solution, no integer validation was

attempted. Each time a filtered solution was obtained, a

fixed solution was also produced. Note that some of the

outliers may be caused by errors in the truth solution.Generally, the fixed solution has better median and

average errors, but occasionally produced outliers when

the filter was starting. However, the fixed integer solution

was generally discarded because of difficulty providing a

reliable uncertainty estimate. Results for the fixed

solution are included in Table 1.

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Warren Tests

The next set of tests was conducted in Warren, Michigan.

The test routes were classified into the following

environment conditions:

1. Freeway with heavy traffic, overpasses, and

submerged roadway

2. Heavily foliated suburban areas3. Open sky suburban areas

Environmental conditions (1) and (2) were combined into

a single route, identified as obscured . A separate route,

identified as o pen sky, covered environmental condition

(3).

Warren Tests—Obscured

The Warren obscured route tested the system under

moderate to difficult GPS visibility conditions. The

obscured route started at GM Technical Center, proceeded

westbound on 13 Mile Road (a moderately foliatedsuburban area), then southbound onto Rochester Road

(heavily foliated), and onto North Main Street in Royal

Oak (a downtown commercial district with two- and

three-story buildings). After passing through downtown

Royal Oak, the route entered westbound I-696, a sunken

freeway with numerous overpasses. The route returned to

the GM Technical Center after taking the Mound Road

exit from I-696. Figure 8 shows a plan view of the Warren

obscured route with outages in areas where the GPS

distance between the two vehicles was not available.

Figure 8. Plan view of Warren obscured route.

Statistics for this route are included in Table 1. Figure 9

shows the range errors for both receiver types. The results

are further discussed in the Performance Evaluation

Summary.


high-cost receiver (blue) for Warren obscured route.

Warren Tests—Open Sky

The Warren open sky route started at GM Technical

Center, proceeded 13 km north on Mound Road, and

returned southbound on Mound Road. This route was an

open multilane road in a suburban/light commercial area.

Traffic conditions were classified as moderate to heavy.

There were occasional trees and utility poles typical of an

urban area. Figure 10 shows the results from the high- and

low-cost receivers in the Warren open sky route.


high-cost receiver (blue) for Warren open sky route.

PERFORMANCE EVALUATION SUMMARY

Table 1 summarizes results from the low-cost and high-

cost GPS systems in all test scenarios discussed above.

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Table 1. High-cost and low-cost GPS test results summary

Route ReceiverEpochs

(2 Hz)

Valid

L1/L2

Solutions

(%)

L1

Solutions(%)

Verified

L1

Solutions

(%)

High 4572 65.35 70.97 63.04Warren

ObscuredLow 4572 65.35 67.38 51.99

High 4011 85.96 99.02 85.96WarrenOpen Sky

Low 4011 85.96 100 85.94

High 2374 67.65 81.04 66.81MenloPark

Low 2374 67.65 77.38 58.47

Fixed

IntegerHigh 2374 67.65 81.04 66.81

Route Receiver

Median

Error

(m)

Ave.

Error

(m)

Errors

< 1 m

(%)

5% Error

(m)

High 0.087 0.131 98.79 0.313Warren

Obscured.Low 0.263 0.467 91.92 1.194

High 0.081 0.096 99.62 0.146WarrenOpen Sky

Low 0.103 0.128 99.56 0.246

High 0.114 0.153 97.35 0.304MenloPark

Low 0.277 0.411 89.63 1.411

FixedInteger

High 0.00 0.054 98.68 0.356

Table 1 illustrates results for the low-cost receiver and the

L1 performance of the high-cost L1/L2 receivers (used for

truth reference). For the Warren open sky route, both

receivers performed very well: over 99% of

measurements with truth references had distance errors of

less than 0.5 meters. Significant differences were most

noticeable at startup and after brief outages when the

vehicles were stationary. Under these conditions, the lack of multipath mitigation technology in the low-cost

receiver led to higher errors. When both vehicles were

moving, the multipath error appears to be more random,

and was successfully averaged out by the filter. These

effects are illustrated in Figure 10, which shows the

distance errors as a function of time for both receivers.

The Warren obscured and Menlo Park routes show more

significant difference between the performance of the

low-cost and high-cost receivers. Also the number of

epochs where solutions were available was much less,

which was attributed to limited satellite visibility in one

or both receivers. The high-cost receiver generallymaintained good accuracy for epochs where a solution

was available. The low-cost receiver usually settled into a

solution within our 1 meter goal, but was frequently

beyond this limit when recovering from an outage. Figure

9 shows distance errors as a function of time for both

types of receivers on the Warren obscured route. Again,

higher errors can be seen as the filter converges after

outages. The multipath rejection capability of the high-

cost receiver again led to a much faster convergence to an

accurate solution.

Figure 11 indicates the duration of the worst 25 outages

on the Warren obscured route for the high-cost L1

receiver. The worst outage lasted about 1 minute, and a

distance of approximately 1 kilometer was traversed

during the outage. The remaining outages fall within the

capability of a tactical grade inertial measurement unit.Outages under 10 seconds can probably be bridged with

wheel sensors and gyros similar to those being used for

stability control systems [Ford et al. 2001, Sinko and

Strus 2001, Shertzinger 2000, Carson 2004].

Figure 11. Duration of the 25 longest outages for the

high-cost L1 receiver.

CONCLUSIONS AND FUTURE WORK

Our choice for the low-cost receiver was based on its

ability to output data synchronized with GPS time pulses.

Without this capability, one must interpolate the data to

achieve ambiguity resolution. This does not present a

significant problem when both receivers are stationary.

However, when both receivers are subject to significant

acceleration, as in the case of automotive safety

applications, interpolating position and velocity can lead

to substantial errors unless a very high sampling rate is

used. With the 2 Hz sampling rate used here, a 30 cm

error could occur in the case of 1 G acceleration. With 10

Hz sampling, the error would be reduced to about 1.5 cm,

which is still significant for attempting to resolveambiguities.

Many modern low-cost receivers utilize large numbers of

correlators for fast reacquisition, greater sensitivity, and

multipath rejection. To the extent these receivers reject

multipath, and not withstanding the time synchronization

issue, we expect these low-cost receiver results to rival

the L1 results for the high-cost receiver used in these

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experiments. It is questionable whether higher sensitivity

will be of much value when accuracy requirements are as

tight as 1 meter. Limited experiments have shown that

accuracy attained with weak signals is poor. Also, the

filter we used requires carrier phase, and the receivers

require several seconds to resolve the half cycle

ambiguity.

The performance evaluation of select low-cost GPSreceivers has shown solution availability ranging from

70–99% in most test routes, with worst availability in

heavily foliated environments. The low-cost system

produced relative horizontal position accuracy of better

than 1 meter for 99% of time in open sky routes. On

obscured routes, 90% of verifiable measurements were

within 1 meter of the reference system, limited by the

time duration when the reference system was able to

provide a solution. This confirms that existing low-cost

GPS technology is capable of meeting the requirements of

road-level and lane-level DAS functionalities.

The most significant ongoing work is focused onimproving reliability and availability of positioning

systems in all environments, especially urban canyons.

While significant improvements have been implemented

in vehicle platforms over the years, there is ongoing work

to make these systems even more reliable and available,

such that these functionalities can be deployed in mass

scale. General Motors is currently working on integrating

various in-vehicle sensors with GPS to further improve

system reliability and availability, particularly in urban

environments. This effort includes investigating new

sensor technologies, as well as integrating latest

technology enhancements to existing systems. General

Motors is also investigating the use of modernized GPS

and Galileo GNSS in future vehicle platforms. Future

GNSS developments are expected to further improve the

reliability and availability of GNSS-based positioning.

While this work is focused on road-level and lane-level

positioning capability, General Motors is also

investigating in-lane-level positioning capability for long-

term applications. New algorithms and processes are

being investigated using existing and future GNSS

configurations.

ACKNOWLEDGMENTS

The authors greatly appreciate the contribution of Mark

Dolan at SRI International who developed the RM

component of the relative positioning test platform.

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