In this paper, a simple deterministic model of driver performance was used to examine kinematics- and perceptual-based rear-end collision avoidance algorithms over a range of collision situations, algorithm parameters, and assumptions regarding driver performance. Results show that the assumptions concerning driver reaction times have important consequences for algorithm performance, with underestimates dramatically undermining the safety benefit of the warning. Further, when drivers sometimes rely on the warning algorithms, larger headways can result in more severe collisions. This reflects the nonlinear interaction among the collision situation, the algorithm, and driver response that should not be attributed to the complexities of driver behavior but to the kinematics of the situation. Comparisons made with experimental data show that a simple human performance model can capture important elements of system performance and complement expensive human-in-the-loop experiments.
Abstract