Human-Machine Shared Steering Decision-Making of Intelligent Vehicles Based on Heterogeneous Synchronous Reinforcement Learning

Human-machine cooperation can simultaneously leverage the strengths of both human drivers and machines, making it a promising solution for improving driving safety, comfort, and experience. This paper designs a heterogeneous synchronous reinforcement learning (HSRL)-based human-machine shared steering decision-making (HMSSDM) strategy for intelligent vehicles. First, the vehicle dynamics, which incorporate steering characteristics, are built to quantify human driver's steering behavior. Additionally, the scenario-oriented driving constraints (SODCs) are established to demonstrate driving constraints from traffic participants, roadside obstacles, and traffic signs. Second, to enhance the rationality and reliability of steering behaviors, the human driver's steering behavior is evaluated using a fuzzy logic strategy, and HSRL is proposed to simultaneously determine steering actions and allocate driving authority between the human driver and machine. The main advantage of HSRL is its ability to perform both continuous domain learning (CDL) and discrete domain learning (DDL) simultaneously. Finally, the proposed method is validated using a human and hardware-in-the-loop (HHiL) experimental platform. The comparison results demonstrate that the proposed method outperforms the comparison methods in terms of driving safety, comfort and experience.