Human-Like Decision Making for Autonomous Driving With Social Skills

There may exist long-term mixed traffic that consists of human-driven vehicles (HDV) and autonomous driving vehicles (ADV). Hence, a formidable challenge arises: the effective decision-making process among these heterogeneous vehicle types. The disparity in the level of decision-making among heterogeneous vehicles is significant. Human driving behaviors and volition, performed in HDV, are speculative and uncertain, while ADV's behavior is unitary and conservative. To address this issue, a human-like decision-making framework for ADV considering social skills is designed, by introducing social value orientation (SVO) which is used to measure the degree of altruism of human drivers, and a sociality-aware Stackelberg game model and a social potential field model are proposed. Firstly, an inverse reinforcement learning (IRL) algorithm is applied to construct a structural cost function about human-driven interactive trajectories in order to estimate the SVO of HDV and endow ADV with the ability to respond to SVO. Secondly, the sociality-aware Stackelberg game approach is designed to capture the social interaction between heterogeneous vehicles, considering personal and public interests. Furthermore, a social potential field model is proposed, and then combined with receding horizon optimization (RHO) to plan socially-skilled trajectories. Finally, three traffic scenarios are used to verify that the developed decision-making algorithm can make safe and socially-skilled decisions in mixed traffic scenarios, in which several cases in terms of HDV with various SVO values are tested to prove the validity of human-like decision making process of an ADV.