A Human-Like Shared Driving Strategy in Lane-Changing Scenario Using Cooperative LPV/MPC

Linear quadratic (LQ) game has been widely used in shared driving to resolve conflicts between drivers and driving automation systems (DAS). However, the system model in LQ game is assumed to be linear time-invariant, which does not consider the effect of changing longitudinal speed on the lateral control. A novel cooperative LPV/MPC approach is proposed, which characterizes the longitudinal and lateral coupled vehicle control in a dynamic game. The stability of the cooperative LPV/MPC system is analyzed in the presence of inconsistent targets of driver and DAS. Driver behavior is studied using 2,701 lane-changing cases extracted from naturalistic driving data (NDD). A human-like risk assessment method is achieved according to the driver behavior characteristics. Finally, a shared driving strategy in lane-changing scenarios is presented based on the cooperative LPV/MPC algorithm and the human-like risk assessment method. Simulation validations show that the shared driving strategy can ensure vehicle stability when longitudinal control, lateral control and dynamic game are performed simultaneously. In addition, the shared driving strategy can realize the driving weight gradual handover, which can help to better assist the driver in risk avoidance.