Scene-aware driver state understanding in car-following behaviors

Abstract

This research represents the heterogeneity in car following by a hidden variable driver state, which could change due to the driver's habit, fatigue, distraction, influence of surrounding traffic etc, resulting in the heterogeneous behaviors of such as fast or slow, strong or weak response to the same level of stimuli. A probabilistic method of driver state understanding is proposed by modeling and reasoning the heterogeneity in car-following behaviors, and the influence of surrounding traffic is addressed explicitly in addition to the leader-follower pair aiming at applications in crowded real-world traffic. Experiments are conducted by using the on-road trajectory data that were collected from motorways in Beijing, where four distinctive driver states and corresponding car-following models are learnt. With online understanding of driver state, the particular car-following model is used to predict the drivers velocity control, where results of improved accuracy are demonstrated.