A risk‐based driver behaviour model - Yuxia Yuan; Xinwei Wang; Simeon Calvert; Riender Happee; Meng Wang (IET Intelligent Transport Systems, 2024)
Abstract Current driver behaviour models (DBMs) are primarily designed for the general driver population under specific scenarios, such as car following or lane changing. Hence DBMs capturing individual behaviour under various scenarios are lacking. This paper presents a novel method to quantify individual perceived driving risk in the longitudinal and lateral directions using risk thresholds capturing the time headway and time to line crossing. These are integrated in a risk‐based DBM formulated under a model predictive control (MPC) framework taking into account vehicle dynamics. The DBM assumes drivers to operate as predictive controllers jointly optimising multiple criteria, including driving risk, discomfort, and travel inefficiency. Simulation results in car following and passing a slower vehicle demonstrate that the DBM predicts plausible behaviour under representative driving scenarios, and that the risk thresholds are able to reflect individual driving behaviour. Furthermore, the proposed DBM is verified using empirical driving data collected from a driving simulator, and the results show it is able to accurately generate vehicle longitudinal and lateral control matching individual human drivers. Overall, this model can capture individual risk perception behaviour and can be applied to the design and assessment of intelligent vehicle systems.