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Vehicle Positioning, 

Navigation, and Timing: 

Leveraging Results from 

EAR Program-Sponsored 

Research

The Exploratory Advanced Research Program

WORKSHOP SUMMARY REPORT • November 2012
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Foreword

To examine dependable, precise, and commercially affordable positioning and navigation for roadways, 

the Federal Highway Administration’s (FHWA) Exploratory Advanced Research (EAR) Program and 

Office of Operations Research and Development convened a panel of Government program managers 

and researchers involved in positioning and navigation. The workshop was held on November 20, 

2012, at the Turner-Fairbank Highway Research Center and titled “Vehicle Positioning, Navigation, and 

Timing: Leveraging Results From EAR Program-Sponsored Research.” It brought together experts who 

deal with research, development, deployment, or regulation of vehicle positioning and navigation for 

increased safety, mobility, and efficiency in transportation systems. 

The panel of experts shared information about the results of EAR Program-sponsored research on vehicle 

positioning and navigation, addressed potential follow-up applied research, and discussed continued 

fundamental research gaps. They looked into summary requirements for determining appropriate 

positioning requirements. The group also identified key government, industry, and academic audiences 

that would be interested in the results as well as ways the EAR Program can help connect the audiences 

with the results. Their findings are presented in this report.

The EAR Program-sponsored research is looking at the next generation applications for vehicle 

positioning and navigation as well as opportunities to apply the results to other transportation modes. 

This research was undertaken to increase mobility on our Nation’s highways and should be of interest 

to State highway agencies, academia, other Government agencies, industry, and the vehicle positioning 

and navigation community. 

Joseph I. Peters

Director, Office of Operations

Research and Development

Debra S. Elston

Director, Office of Corporate 

Research, Technology, and 

Innovation Management

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation in the 

interest of information exchange. The U.S. Government assumes no liability for the use of the information 

contained in this document. The U.S. Government does not endorse products or manufacturers. 

Trademarks or manufacturers’ names appear in this report only because they are considered essential 

to the objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve Government, 

industry, and the public in a manner that promotes public understanding. Standards and policies are 

used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA 

periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality 

improvement. 

Cover image: © shutterstock.com/Sergei Nivens
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Technical Report Documentation Page
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Table of Contents

Introduction 1

Part One: Presentations 4

Next Generation Vehicle Positioning—David Bevly 5

Innovative Approaches for Next Generation Vehicle Positioning—Jay Farrell 10

Part Two: Next Steps 13

Disseminating Project Results and Future Research Needs—David Kuehn 14

Appendix A 16

Agenda 17

Appendix B 18

List of Attendees 19
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LIST OF FIGURES

Figure 1. Diagram. The study fuses outputs from various technologies.

Figure 2. Diagram. The subsystem capability analysis matrix.

Figure 3. Chart. Penn State University’s road fingerprinting concept.

Figure 4. Photo. LIDAR Base Lane Detection.

Figure 5. Chart. LIDAR Base Lane Detection.

Figure 6. Diagram. Stanford Research Institute’s Visual odometry concept.

Figure 7. Photo. The National Center for Asphalt Technology’s oval track.

Figure 8. Diagram. The capabilities of aiding sensor categories.

Figure 9. Diagram. Positioning uncertainty reduction.

Figure 10. Photo. Vehicle equipped with sensor platform on roof.

Figure 11. Test vehicle outside the Turner-Fairbank Highway Research Center.

List of Tables

Table 1. Outputs of various systems.
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1

Introduction

On November 20, 2012, at the TurnerFairbank Highway Research Center (TFHRC) in McLean, VA, the Federal 

Highway Administration’s (FHWA) Exploratory 

Advanced Research (EAR) Program and Office 

of Operations Research and Development (R&D) 

convened a workshop to share information about 

the results of EAR Program-sponsored research 

on vehicle positioning and navigation. 

The workshop, titled “Vehicle Positioning, 

Navigation, and Timing: Leveraging Results 

From EAR Program-Sponsored Research,” was 

held to identify key government, industry, and 

academic audiences who would be interested 

in the results and how the EAR Program can 

assist in connecting the audiences with the 

results. It provided an opportunity to discuss 

potential follow on applied areas of research in 

addition to addressing continued fundamental 

research gaps that still need to be resolved to 

provide dependable, precise, and commercially 

affordable positioning and navigation for 

roadways.

The audience included Government program 

managers and researchers involved in the 

research, development, deployment, or 

regulation of positioning and navigation for 

increased safety, mobility, and efficiency in 

transportation systems.

The workshop began with a welcome from Joe 

Peters, Director, Office of Operations R&D at 

TFHRC. Peters described the work of the Saxton 

Transportation Operations Laboratory at TFHRC. 

He stated that the Saxton Laboratory 

includes exploratory work in automated 

systems and described the three teams that 

comprise the Office of Operations R&D as 

follows:

• Transportation Enabling Technologies.

• Transportation Operations Concepts and 

Analysis.

• Transportation Operations Applications.

This Vehicle Positioning, Navigation, and 

Timing Workshop was sponsored by the 

Transportation Enabling Technologies Team. 

Peters highlighted the vital importance of 

position, navigation, and timing (PNT) 

technologies. As an example, without a 

global positioning system (GPS) signal 

providing a reliable timing reference, traffic 

signals would lose the ability to function in a 

coordinated way, resulting in widespread 

traffic jams.

David Kuehn, EAR Program Manager, stated 

that FHWA’s research is mission-driven to 

deliver mobility to the Nation’s highways. 

The EAR Program looks for solutions 

beyond transportation, exploring the fields 

of science and engineering for nextgeneration applications, but remains ever 

mission-focused. 

Several EAR Program-funded projects are 

related to positioning and navigation. The 

results of two of these projects were 

presented at the workshop and are detailed 

in this report. Although these projects are 
2

highway-focused, this workshop serves as an 

opportunity to apply these results to other 

transportation modes.

An overview of the two projects was then 

presented. Jim Arnold introduced the University 

of California at Riverside’s research on new 

approaches in vehicle positioning. He stressed 

that one of the most importation aspects in 

research is a better definition of requirements. 

David Gibson introduced Auburn University’s 

research on vehicle positioning in GPSdegraded environments. He emphasized the 

importance for having calibrated test tracks in 

test environments.

Gary Pruitt of ARINC and Scott Andrews of 

Cogenia Partners then presented a talk on 

“Position Technology and Requirements for 

Connected Vehicles.” An assessment on 

accuracy and positioning errors was presented. 

The focus of the talk was mainly on positioning 

errors and methods to improve accuracy. 

Pruitt and Andrews attempted to define a 

process for systematically deriving 

requirements for positioning requirements for 

a given application. To accomplish this, there 

needs to be a better understanding of 

positioning errors and methods for improving 

position estimates. There are two main types of 

errors—random and offset.

• Random position errors cause position 

estimates to change from measurement to 

measurement. These errors are specified in 

terms of a statistical representation.

• Offset errors will affect all receivers of the 

same type in the same general manner. These 

errors are either systematic or ionospheric. 

They can be minimized by using differential 

GPS.

Other errors were also discussed, including 

motion errors. From moving platforms, a person 

only gets one sample at a given point instead of 

at a distribution of points. Latency errors are 

small time errors between platforms. These can 

be fixed with synchronized clocks. There may 

also be a processing lag caused by the use of 

algorithms to help smooth variations in position 

measurements. Map errors—where landmark 

positions are incorrect—were also discussed.

The talk then focused on decision errors, in 

which measurement and reality are compared. 

Correct decisions are made when measurement 

and reality are the same, whereas decision 

errors occur when measurement and reality are 

not the same. These error conditions cause 

false positives and false alarms. A false positive 

(i.e., when the measurement indicates true

when in fact the reality is false) can lead to a 

missed detection or a dangerous situation. A 

false negative (i.e., when the measurement 

indicates false when in fact the reality is true) 

can lead to a nuisance or false alarm.

Next presented was a discussion of error 

tolerance and tolerable errors. In practical 

systems, some level of a tolerable error needs 

to be defined. These tolerances are different for 

false positives and false negatives, with a much 

larger tolerance for false positives. 

The presentation then focused on hazard 

analysis, in which severity, exposure, and 

controllability determination were discussed. 

Severity was defined as a range that spanned 
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from no injuries to life-threatening injuries, 

the term exposure varied from an incredibly 

low to a high chance of occurrence, and 

controllability indicated a range that 

spanned from generally controllable to 

difficult to control. Severity, exposure, and 

controllability determinations could be 

measured by a probability of occurrence.

The talk concluded with a summary of 

requirements and a summary of positioning 

technology. The presenters recommend the 

development of a decision–error method 

for determining appropriate positioning 

requirements. They also identified additional 

requirements based on dynamic factors 

and suggested time syncing to determine 

relative positioning. The summary of the 

presenters’ review of positioning 

technologies identified GPS as the most 

promising (other technologies are less 

accurate and require significant 

infrastructure). They stressed that there is 

an emerging interest by GPS makers to 

explore low-cost, high-accuracy vehicle 

solutions.

Several questions were asked in response to 

the presentation. One question pertained to 

the scale of production or potential market 

for purchasing positioning technology. The 

response involved a discussion on receivers 

used for survey instruments. There is 

marginal additional cost for the hardware, 

but companies may charge more for 

additional performance as a way of 

recouping development costs. For example, 

some receivers are sold with full capabilities 

and users purchase licenses to activate the 

desired features. Further discussion 

pertained to errors and latency and how to 

develop budgets for cumulative errors.
PART ONE:

PRESENTATIONS
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Next Generation Vehicle Positioning

David Bevly

Auburn University

David Bevly along with three graduate 

students (Jordan Britt, Chris Rose, and 

Scott Martin) from Auburn University 

presented a talk on the status of Auburn’s 

next generation vehicle positioning. The 

objective of the project was to provide 

ubiquitous precise positioning in regard 

to vehicle safety and automation in the 

presence of GPS degradation. Auburn 

partnered with Kapsch TrafficCom, Penn 

State University, and Stanford Research 

Institute (SRI) on this project. The project 

scope was to assess diverse positioning 

and data-fusion techniques, characterize 

achievable accuracy and robustness, and 

test and demonstrate capabilities on test 

track and roadway scenarios. The technical 

approach of this study was to fuse outputs 

from various technologies in an extended 

Kalman filter, which is used to smooth 

multiple measurements, exploit accuracy, 

and mitigate faults, as shown in figure 1.

Each technology was analyzed based on 

cost, availability, a six degrees-of-freedom 

(DOF) position, a three DOF position, 

drifting, environmental influences, and a 

requirement for an infrastructure, map, 

and central processing unit (CPU). A chart 

showing the results of this analysis for each 

examined technology is included in figure 2.

The first two navigation technologies 

presented were GPS and an inertial 

navigation system (INS). GPS fared well 

in availability, three DOF positioning, and 

drifting solution. It did not satisfy a six 

DOF position. INS satisfied the cost, six 

and three DOF positioning, and did well 

in denied environments, although it had a 

drifting problem. GPS and INS integration 

can satisfy all of the listed capabilities.

Jordan Britt presented Penn State 

University’s road fingerprinting concept, as 

Figure 1. Diagram. The study fuses outputs from various technologies.

© Auburn University
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Figure 2. Diagram. The subsystem capability analysis matrix.

(NOTE: DOF = degrees of freedom, CPU = central processing unit, PSU = power supply unit, 

AU-LDW = Auburn lane-departure warning, LIDAR = light detection and ranging, SRI = SRI International.)

Data were bound and filtered. They 

conducted the tests during both day and 

night on country roads that had no outside 

lane markings. After post processing, the 

researchers found that the final results 

were quite accurate. The overall results of 

LIDAR processing were that it did not have 

a drifting problem and did not require an 

in-place infrastructure. The LIDAR solution 

does not provide a six DOF solution. 

shown in figure 3. To use road fingerprinting, 

the road needs to have been surveyed. This 

is accomplished by driving with a high-grade 

inertial measurement unit (IMU) and a real 

time kinematic (RTK) GPS. The road survey 

must produce a map of the pitch signal, 

and the road fingerprinting is accomplished 

using a pitch gyro, wheel odometry, and the 

previously generated map. Required hardware 

is not a problem, because pitch gyro and wheel 

encoders currently exist on most automobiles. 

The capability analysis profile of this method 

is positive for cost, current availability, has 

no environmental influences, and requires no 

specific infrastructure to be in place. It does 

not provide a six DOF solution.

Jordan also presented Auburn’s research into 

the use of light detection and ranging (LIDAR) 

in lane detection warning systems. Researchers 

conducted LIDAR tests by measuring the 

reflectivity on road markers and road edge 

detection, as shown in figure 4 and figure 

5. They tested lane detection under various 

weather conditions and with various road 

conditions. Results were filtered and averaged. 

Except under rain conditions, the results were 

very good. For road edge detection, they used 

both distance and estimation of reflectivity.

© Auburn University

Figure 3. Chart. 

Penn State University’s road fingerprinting concept.

© Auburn University
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Next was Chris Rose’s presentation on the 

use of cameras in lane detection. Cameras 

already exist in newer vehicles. With no side 

markings on roads, color is used to determine 

the edge. This is difficult at night or in the 

shade. Edge detection is determined by 

using methods such as a Hough transform, 

least squares interpolation, Kalman filtering, 

and determining polynomial bounds. 

Researchers performed the test with a 

Webcam at low resolution, with a road 

width measurement taken far down the 

road, in day and night scenarios, and with 

induced error sources, including shadows, 

headlights, and road intersections. They 

compared the results of these tests with 

the physical conditions. The overall results 

for camera road edge detection were a 

low-cost solution with currently available 

technology, having no drift or infrastructure 

requirements. The camera road edge 

detection system did not provide a six DOF 

solution.

Chris went on to present results from testing 

of the SRI visual odometry system, shown in 

figure 6. This process tracks features from

image to image and extracts egomotion, 

providing local odometry without GPS 

initialization. The sensor package includes 

the following components: two cameras 

with Ethernet interfaces, two lenses, an IMU 

operating at 100 Hz, a Netgear Ethernet 

hub, a computer, cabling, and connectors. 

The cameras are rear-mounted to minimize 

© Auburn University

© Auburn University

Figure 4. Photo. LIDAR Base Lane Detection.

Figure 5. Chart. LIDAR Base Lane Detection.

© Auburn University

Figure 6. Diagram. 

Stanford Research Institute’s Visual odometry concept.
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glare problems. Researchers tested the 

system in inclement weather during which 

feature tracking and positioning remained 

functional and lenses covered with water 

droplets cleared once the vehicle reached 

higher speeds. The researchers determined 

that hoods overhanging the lenses might be 

sufficient to reduce the effects of sun glare and 

water droplets. The overall results from testing 

the SRI visual odometry system were a three 

DOF solution with no requirements for maps 

or specific infrastructure requirements. There 

were no critical problems with this solution.

The final system presented was the KapschGantry TrafficCom—a dedicated short-range 

communications (DSRC) system. The initial 

plan was to estimate the range based on the 

turnaround time for unsynchronized clocks. 

It was found that the project hardware was 

not capable of lane-level precision; however, 

the sensor may still provide some information 

if nothing else is available. Researchers at 

the National Center for Asphalt Technology 

(NCAT) test track collected time of flight data 

between the Kapsch radio base station and 

the Auburn test vehicle. The resulting variation 

in time of flight measurements proved to be 

insufficient for lane-level measurements. The 

overall results for the Kapsch TrafficCom 

system provided a three DOF, had no drifting 

problem, did not require a map or CPU, and 

was free of environmental issues. It did not 

provide a six DOF position.

Scott Martin then presented results of 

integration of the various systems. The INS 

was used as the base system with data from 

the other sensors fused in an extended 

Kalman filter implementation. Eighteen 

states were propagated from this scenario 

by using nonlinear relationship and IMU 

measurements. The outputs of the various 

systems are detailed in table 1.

Integration testing in Detroit, MI, was then 

discussed. Honda developed the test route 

to meet road-use class proportioning 

of vehicle travel in the United States.1

Environments found at this test track 

included trees, tree canopies, overpasses, 

buildings, urban canyons, and tunnels. 

Testing scenarios included testing various 

combinations of sensors and an extended 

Kalman filter implementation. The 

1. U.S. Department of Transportation, Federal Highway 

Administration, “Annual Vehicle-Miles of Travel 1980-

2007, By Functional System, National Summary (Table 

VM-202, summary for 2007),” Jan. 2009, http://www.

fhwa.dot.gov/policyinformation/statistics/vm02_summary.cfm.

© Auburn University

Table 1. Outputs of various systems. 

 (NOTE: INS = inertial navigation system, GPS = global 

positioning system, LDW = lane-departure warning, 

LIDAR = light detection and ranging.)
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results showed that GPS/INS integration 

improved results in heavy foliage and 

urban canyons, and vision updates were 

provided where the lane of travel was 

assumed. Further observations showed 

that, although subsystem integration 

improved positioning accuracy, it was 

limited by maps, survey accuracy, and 

availability. Limitations were identified on 

road fingerprinting and visual odometry. 

A new lane detection algorithm is needed 

that leverages new road edge detection 

methods and inertial information.

Testing at NCAT was discussed. The facility 

has a 1.7-mi (2.7 km) oval track, shown in 

figure 7, and is surveyed for lane markings 

and centers. The RTK base station supports 

wireless communications. Researchers 

collected four data sets of several laps. The 

results showed that a full system of sensors 

performed best, followed by a GPS sensor. 

The full system contained vision and 

fingerprint aiding, which improved lanelevel accuracy. The GPS/INS integrated 

solution trailed as a result of memory limits.

Rose discussed the results of testing 

on driveways at TFHRC. A Novatel base 

station provided RTK corrections. Satellite 

visibility was degraded in some areas. The 

results showed limited precision in the 

fingerprint survey and lane-level accuracy 

was best with GPS/INS integration because 

of error correlation.

Conclusions of the Auburn studies were 

that subsystems help improve lane-level 

accuracy and continued testing is needed to 

assess system robustness. During a questionand-answer session, one participant of 

the workshop asked whether testing had 

been conducted with Jersey barriers. The 

response was that testing had not been 

conducted with Jersey barriers since Jersey 

barriers are not common near rural Auburn.

Figure 7. Photo. The National Center for Asphalt Technology’s oval track.

© Auburn University
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Jay Farrell started his presentation with 

a discussion of real-time positioning 

and precision mapping. Many intelligent 

transportation applications require 

positioning with high degrees of accuracy, 

a high sample rate, and working in diverse 

environments at low cost. A solution to these 

requirements is to fuse high rate sensors, 

such as an encoder or IMU, with lower rate 

sensors, such as GPS, cameras, LIDAR, radio 

detection and ranging (RADAR), DSRC, and 

signals of opportunity. A chart showing the 

capabilities of the aiding sensors can be 

found in figure 8.

These aiding sensors have characteristics 

that help reduce the position uncertainty. 

For example, in an urban canyon with 

one GPS satellite available, the car sensor 

system can only see ahead. With the use 

of traffic signals, the system can make 

distance adjustments as the car travels 

perpendicular to the signal, as shown 

in figure 9. With LIDAR, there is access 

to raw data; with RADAR, raw data are 

inaccessible, only position data—range and 

angle—are available. Therefore, RADAR 

needs mapped features on which to plot 

these positions for the data to be useful.

Innovative Approaches for Next Generation Vehicle Positioning

Jay Farrell

University of California at Riverside

Figure 8. Diagram. The capabilities of aiding sensor categories.

© University of California at Riverside

 (NOTE: GPS = global positioning system, DGPS = differential global positioning system, CPDGPS = carrier-phase 

differential global positioning system, TOA = time of arrival, TDOA = time difference of arrival, AOA = angle of 

arrival, GNSS = global navigation satellite system, TRN = terrain reference navigation, FB = feature based.)
11

Farrell then focused on precise roadway 

feature maps. These features include road 

and lane edges, sign types and locations, 

street lights, traffic signals, and stop bars. 

High precision (sub-decimeter) positioning 

is needed for next generation applications. 

Some of these applications could include 

lane-departure warnings, curve-over speed 

warnings, signal phase and timing by lane, 

intersection management, and collision 

avoidance.

The mapping process was discussed. The first 

step is data acquisition from vision systems—

GPS/INS and LIDAR. The data are gathered 

from these systems as the vehicle, shown in 

figure 10, is driven. Data are then smoothed 

to provide a continuously smooth trajectory. 

Features are then identified and extracted 

from the raw data. In the final step, relative 

map features are combined with absolute 

trajectory and placed in a geographic 

information systems (GIS) database in 

world coordinates.

No single independent sensor technology 

is capable of simultaneously attaining 

the accuracy, integrity, and availability 

specifications for lane-level positioning in 

expected diverse environments. Integrated 

positioning, which fuses asynchronous data 

from diverse sensors, is the best approach 

to reliably and accurately estimate vehicle 

position. Inertial Navigation Systems and 

Encoder Navigation Systems provide 

positioning solutions in all environments 

continuously at high rates; however, their 

accuracy drifts over time without aiding.

Global navigation satellite systems provide 

high accuracy in open areas where 

satellite signals can be received; however, 

performance degrades in dense urban 

areas. Feature-based navigation-aiding 

Figure 9. Diagram. Positioning uncertainty reduction.

© University of California at Riverside

Figure 10. Photo. 

Vehicle equipped with sensor platform on roof.

© University of California at Riverside
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by using a camera, LIDAR, or RADAR can 

be successful when mapped features can 

be reliably detected and tracked. Several 

forms of ground-based radio communication 

systems, which can offer potentially useful 

position information, have been designed to 

penetrate the urban infrastructure and have 

the added advantage that their performance 

characteristics can still be influenced by the 

engineering community interested in roadway 

applications. 

Farrell concluded the presentation by 

presenting ideas for future work:

Positioning

• DSRC modem-based time-of-flight positioning 

(direct contacts with manufacturers are in 

place).

• Terrestrial Radio Frequency Signals of 

Opportunity: Digital television, digital radio, 

and cellular.

• Enhance performance and robustness of 

feature-based methods, especially RADAR 

and LIDAR.

• Investigate safety- and integritymonitoring for data-fused vehicle 

applications.

Mapping

• Improved and more fully automated 

mapping processes. 

• Mapping additional feature types. 

• Thorough evaluation in less-structured, 

more-dynamic environments. 

• Maintenance of the precision map. 

 • Crowd sourcing.

 • Targeted updates. 

• Large-scale computer or cloud implementation to map larger environments.

Positioning and Mapping

• Thorough evaluation in less-structured, 

more-dynamic environments. 
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PART TWO: NEXT STEPS
14

Disseminating Project Results and Future Research Needs

David Kuehn

EAR Program, FHWA

David Kuehn initiated a discussion on 

sharing project results. Kuehn asked, from 

maps to ITS, what data can be shared?

It was stated that data from the Connected 

Vehicle Safety Pilot Program should be 

available soon. This will allow users to 

examine the formats of data so that they 

can best use the data.

The question of what is “state of the art” 

was also put forward. It was noted that 

the commercial world is now deploying, at 

a reasonable price, some of the concepts 

presented today. 

Discussion proceeded to cover the IMU in 

long vehicles (e.g., buses and trucks) and 

the sensitivity issue and problem of losing 

the GPS signal. It was highlighted that in 

sharp turns, the distance from the center 

of gravity or rotation could be issues. Jay 

Farrell stated that center of gravity is not 

as important as using the center of axle 

position. It was suggested that robust 

control systems are needed and that 

multiple sensors are required to assist 

these issues.

Several mapping issues that concern 

intersection geometry were discussed as 

follows:

• Location of stop line—How often does it 

change? Who updates the information?

• Striping—Put a GPS on a striping machine.

• Next time the intersection is reconfigured 

or re-striped, measure it digitally.

• Standards are needed.

Bridge height was put forward as another 

issue, because it is not correctly mapped 

or displayed. In addition, truckers may not 

know the correct height of their vehicles. 

Test sites were also discussed, and it was 

noted that there was limited instrumented 

data from Michigan; the Honda site is the 

only one with a surveyed urban forest. It is 

important to have a greater variety of test 

sites or to have one test site that has great 

variety of test scenarios.

There was a discussion on having a larger 

follow-up workshop on positioning and 

navigation; however, it was also suggested 

that the commercial world may solve these 
15

problems before this group gets around to 

discussing them.

Other discussion topics included lane control 

and intersection control, the need for living 

laboratories, and the need of test beds to 

simulate age-of-road conditions.

It was also suggested that there should be 

more collaboration, including the potential 

for Auburn and Riverside to run each other’s 

tests. Moreover, to cut down on testing costs, 

perhaps rental cars with mounted devices 

could be used.

During discussion, it was noted that no sensors 

talk to each other in modern-day automobiles. 

It was suggested that these sensors could 

Figure 11. Photo. Test vehicle outside the 

Turner-Fairbank Highway Research Center.

communicate via the controller area 

network data bus or some other datasharing mechanism. In addition, there 

needs to be time-stamped raw data.

The final discussion pertained to what 

research government should be involved 

in. The question of whether the lack of 

inoperability is because of lack of interest 

or a problem with future competitors was 

also highlighted.

The meeting ended with a tour of the 

laboratory and an opportunity to ride in the 

Auburn University test vehicle, pictured in 

figure 11.
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Appendix A
17

Agenda

Vehicle Positioning, Navigation, and Timing: 

Leveraging Results from EAR Program-Sponsored Research

Turner-Fairbank Highway Research Center, McLean, VA, November 20, 2012

9:15 a.m. Welcome and Introductions

 Joe Peters, Director, Office of Operations R&D, FHWA

9:30 a.m. Exploratory Advanced Research Program Context

 David Kuehn, EAR Program Manager, FHWA 

 Overview of EAR Projects

 Jim Arnold, Office of Operations R&D, FHWA

 David Gibson, Office of Operations R&D, FHWA

9:45 a.m. ITS Vehicle Positioning Requirements

 Gary Pruitt, ARINC

10:30 a.m. Break 

10:45 a.m. Research Results 

 Next Generation Vehicle Positioning in GPS — Degraded 

 Environments for Vehicle Safety and Automation Systems

 David Bevly, Auburn University 

 Innovative Approaches for Next Generation Vehicle Positioning

 Jay Farrell, University of California at Riverside 

12:30 p.m. Laboratory Visit and Lunch

2:00 p.m. Next Steps: Disseminating Project Results

 David Kuehn, EAR Program Manager, FHWA

3:15 p.m. Break 

3:30 p.m. Future Research Needs

 David Kuehn, EAR Program Manager, FHWA

4:45 p.m. Close
18

Appendix B
19

List of Attendees

Bruce Abernathy

Senior Principal Engineer

ARINC

Scott Andrews

Technical Partner

Cogenia Partners, Inc.

James Arnold

Electronic Engineer

Office of Operations R&D, FHWA

David M. Bevly

Professor

Auburn University

Jordan Britt

Student

Auburn University

Jay A. Farrell

Professor

University of California, Riverside

Walton Fehr

Program Manager

ITS Joint Program Office, RITA

Robert Ferlis

Technical Director

Office of Operations R&D, FHWA

David Gibson

Highway Research Engineer

Office of Operations R&D, FHWA

Helen Gill

Program Director

National Science Foundation

Terry Halkyard

Program Coordinator

EAR Program, FHWA

Cem Hatipoglu

Transportation Specialist

Federal Motor Carrier Safety 

Administration

David Kuehn

Team Director

EAR Program, FHWA

Mike Lukuc

Program Manager

National Highway Traffic Safety 

Administration 

Scott Martin

Student

Auburn University

Jules G. McNeff

Vice President

Overlook Systems Technology

Rudy Persaud

Highway Research Specialist

Office of Operations R&D, FHWA

Joe Peters

Director

Office of Operations R&D, FHWA

James Pol

Team Lead

ITS Joint Program Office, RITA
20

Gary Pruitt

Senior Director

ARINC

Anura Rabel

HR Information Specialist

Office of Human Resources, FHWA

William Rabinovich

Optical Sciences Division

U.S. Naval Research Laboratory

Christopher Rose

Student

Auburn University

Jayne Rossetti

Senior Software Engineer

Volpe Center, RITA

Stephen Stasko

National Highway Traffic Safety 

Administration 
Office of Operations Research and Development 

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-13-052

HRTM-30/5-13 (WEB)E