Agent-based evaluation of driver heterogeneous behavior during safety-critical events

Heterogeneous driver behavior during safety-critical events is more complicated than normal driving situations and is difficult to capture by statistical models. This paper applies an agent-based reinforcement learning method to represent heterogeneous driving behavior for different drivers during safety-critical events. The naturalistic driving data of different drivers during safety-critical events are used in agent training. As an output of the Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) training technique, behavior rules are embedded in different agents to represent heterogeneous actions between drivers. The results show that the NFACRL is able to simulate naturalistic driver behavior and present heterogeneity.