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Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator期刊论文

作者: Lee John D McGehee Daniel V Brown Timothy L Reyes Michelle L
DOI:10.1518/0018720024497844

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页码: 314-334
被引频次: 270
出版者: Human Factors and Ergonomics Society,SAGE Publications,HUMAN FACTORS SOC
ISSN: 0018-7208
卷期: Volume:44    Issue:2
语言: English
摘要: Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly – and thereby avoid more collisions – than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.
相关主题: PERIPHERAL-VISION, PSYCHOLOGY, APPLIED, AUTOMATION, BEHAVIORAL SCIENCES, ERGONOMICS, TRUST, PSYCHOLOGY, Accidents, Traffic - psychology, United States, Computer Simulation, Humans, Middle Aged, Attention, Adult, Female, Male, Reaction Time, Automobile Driving - psychology, Accidents, Traffic - prevention & control, Automobiles, Collision avoidance, Safety and security measures,

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