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

2002/06/01 by John D. Lee, Daniel V. McGehee, Timothy L. Brown +1 · 567 citations
Engineering · Psychology · #Aeronautics #Automotive engineering #Autonomous Vehicle Technology and Safety #Brake #Collision #Computer science #Computer security #Distraction #Driving simulator #Engineering #Human-Automation Interaction and Safety #Poison control #Psychology #Simulation #Traffic and Road Safety #Warning system

paper · doi:10.1518/0018720024497844

published in Human Factors The Journal of the Human Factors and Ergonomics Society 44(2), 314-334 (SAGE Publishing)

openalex publication_date 2002/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

Abstract

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.

Citations

• Human Performance Models and Rear-End Collision Avoidance Algorithms

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