Modelling the lateral dimension of vehicles movement: a stochastic differential approach with applications

Abstract

A stochastic lateral movement model is proposed to address the limitations of current traffic models, which fail to capture the stochastic nature of the lateral component in vehicle movement during lane keeping and lane changing. This model incorporates a lateral noise component and a lateral movement component, with parameters that have clear physical interpretations including noise intensity, driver’s sensitivity to lateral deviation, and sensitivity to noise. The model successfully describes the real-world distribution and standard deviation of lateral displacement, achieves over 70% accuracy in distinguishing between human driven vehicles and autonomous vehicles, derives the lane changing duration distribution consistent with experimental observation, and shows that the sensitivity to lateral deviation is about 7 times higher in lane changing compared to lane keeping.