Trajectory planning for vehicle collision avoidance imitating driver behavior

Abstract

To ensure that autonomous vehicles satisfy the requirements of the traffic environment, vehicle driving ability, and desired driver experience during obstacle avoidance, this paper proposes a trajectory planner that considers three aspects: driving passability, regional safety, and driving acceptance. Multiresolution state lattices and Bézier curve fitters are applied to a state lattice framework to generate candidate obstacle avoidance trajectory clusters. Trajectory evaluation is then carried out in the above three aspects by using trajectory passability, safety and driver behavior proximity, and a trajectory evaluation function is designed to evaluate and screen trajectory clusters. The trajectory passability is checked by the vehicle motion capability set, which is established based on the vehicle dynamics model. The trajectory safety is evaluated by the potential field function between the fitted trajectory and the vehicle driving environment boundary with consideration of the inevitable collision state. The parameters of the vehicle motion state for the fitted trajectory are matched with the driving data of real drivers with different driving styles to evaluate the proximity between the trajectory and driver behavior. The rationality and effectiveness of different driving styles of trajectory planners are verified by vehicle tests under different vehicle velocities and different obstacle disturbances.