Driver’s Individual Risk Perception-Based Trajectory Planning: A Human-Like Method

Lane-changing is a critical issue for autonomous vehicles (AVs), especially in complex environments. In addition, different drivers have different handling preferences. How to provide personalized maneuvers for individual drivers to increase their trust is another issue for AVs. Therefore, a framework of human-like path planning is proposed in this paper, considering driver characteristics of visual-preview, subjective risk perception, and degree of aggressiveness. In the decision making module, a model is built to select the most suitable merging spot, with respect to safety factors and the driver’s degree of aggressiveness. And a novel environmental potential field (PF) suitable for arbitrary road structures is designed to describe the driver’s individual risk perception. In the trajectory planning module, a model predictive control (MPC) based path planner is designed according to the decisions in coincidence with the driver’s individual intentions of collision avoidance. Simulation results have demonstrated that the proposed path planner can provide with personalized trajectories for different combinations of driver preferences and steering characteristics, in scenarios of curved roads with different risks of collision.