Safety

The evaluation of a truck-based drowsy driver warning system through field operational test (FOT) data will provide an objective assessment of user- and device-provided performance data. The evaluation will address five areas: safety benefits, driver acceptance, fleet management acceptance, performance and capability, and deployment. This paper reviews the goals and objectives that the evaluation will pursue. Although the details of the plan will evolve as the FOT approaches, the information provided addresses the main topics and indicates the thoroughness with which the evaluation will be performed. Ultimately, the FOT and its subsequent evaluation will provide a useful contribution to public safety.

National Highway Traffic Safety Administration

Volpe National Transportation Systems Center

Presented at the ITS America Annual Conference and Exposition, April 29 –May 2, 2002 Long Beach, California

A discrete-time model, which characterizes a driver’s braking behavior, is developed. According to the proposed model, the amount of braking depends on the current vehicle speed and the required stopping distance. The model is used to simulate the performance of the NHTSA (National Highway Traffic Safety Administration) Alert Algorithm. The simulation results indicate that, in the situation where an inattentive driver is approaching a stopped lead vehicle at 60 mph, the probability of collision is less than 17.6% when the NHTSA Alert Algorithm is in minimum sensitivity mode. In maximum sensitivity mode, the probability of collision is less than 3.2%.

The Johns Hopkins University - Applied Physics Laboratory

Presented at the ITS America Annual Conference and Exposition, April 29 –May 2, 2002 Long Beach, California

As an example of the newly established ITS Archive Data User Service (ADUS), this study utilizes information technologies to archive incident data collected by NAVIGATOR, Georgia’s ITS system, which makes it feasible to incorporate sophisticated speed contour and incident mappings to unveil the possible causal factors of secondary accidents. This study brought out a new definition of secondary accidents: an accident is a secondary accident if it falls in the congested contour area caused by the initial incident. Preliminary findings in this study demonstrated the suitability and usability of such a definition. State Department of Transportation and local transportation agencies could exploit the possible benefit of eliminating all the secondary accidents to enhance the support for the deployment of Intelligent Transportation System.

Georgia Institute of Technology

Presented at the ITS America Annual Conference and Exposition, April 29 –May 2, 2002 Long Beach, California

Vehicle crash statistics in the U.S. are dominated by light vehicles at a rate in excess of 94% of all vehicles involved. This paper reviews the USDOT crash avoidance program for light vehicles being managed through the Intelligent Vehicle Initiative (IVI) within the Department’s Intelligent Transportation Systems Program.

The paper begins with crash statistics to define the crash problem for light vehicles in the U.S. Four major crash categories dominate the crash statistics, these being rear-end, lane change, road departure, and crossing path crashes (at junctions). Further, these categories are broken down into a total of 15 common pre-crash scenarios along with causal factors. The causal factors analyses identify driving task errors as the primary cause in about 75% of all crashes studied, thus indicating that driver assistance countermeasures could be very effective.

The paper next discusses the IVI programmatic approach to develop crash countermeasures for light vehicles, which requires a joint cooperative effort between Government and industry. The technical approach is described next, which is composed of baseline driver performance analyses, system performance specifications development, creation of human factors design guidelines, objective test development for countermeasure systems, and benefits analyses for the countermeasures.

The last section of the paper describes the current state of progress in each of the areas of the technical approach. Radar-based rear-end collision warning is currently the most mature of the countermeasures and is in final development for a field operational test to begin in 2002. Testbed vehicles are described for each of the four crash countermeasures. Progress in benefits methodology for the countermeasure evaluations is described.

Intelligent Vehicle Initiative - National Highway Traffic Safety Administration

Presented at the 10th ITS Annual Conference and Exposition, May 1-4, 2000 Boston, MA

TRW has built, under NHTSA contract, a fully automated data acquisition system for assessing the effectiveness of a lane change collision avoidance system. The system features two subsystems that allow for automated acquisition and analysis of data. The first is a scanning laser rangefinder which serves both as the collision warning sensor and collector of ground truth information. The second subsystem is an eye-tracker which outputs, at 30 Hz, the driver’s gaze direction. Preliminary data, taken at a test track and on local freeways using team members, have refined the parameters that make up the driver warning algorithm. The data is consistent with a keep out zone adjacent to and 30 ft. behind the car and a 3 second time to arrival for fast approaching vehicles.

Battelle Human Factors Transportation Center

SAIC - Turner-Fairbank Research Center

Federal Highway Administration - Turner-Fairbank Research Center

University of Iowa - Department of Industrial Engineering

Presented at the 10th ITS Annual Conference and Exposition, May 1-4, 2000 Boston, MA