Interacting with Human Drivers: Human-like Driving and Decision Making for Autonomous Vehicles

In this paper, a human-like driving and decision-making framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, based on the driving simulator, the proposed algorithm is evaluated with the human-in-the-loop experiments.