Human performance models and rear-end collision avoidance algorithms

摘要

Collision warning systems offer a promising approach to mitigate rear-end colli- sions, but substantial uncertainty exists regarding the joint performance of the driver and the collision warning algorithms. A simple deterministic model of driver performance was used to examine kinematics-based and perceptual-based rear-end collision avoidance algorithms over a range of collision situations, algo- rithm parameters, and assumptions regarding driver performance. The results show that the assumptions concerning driver reaction times have important con- sequences for algorithm performance, with underestimates dramatically under- mining the safety benefit of the warning.