Motion Planning and Control of Autonomous Driving Intelligence System Based on Risk Potential Optimization Framework

ABSTRACT: This study proposes a motion planning and control system based on collision risk potential prediction characteristics of experienced drivers. Recently, automatic braking systems have been deployed in current automotive markets. However, the existing systems cannot avoid collisions in critical scenario such as a pedestrian suddenly darting out from a poor-visibility blind corner. By optimizing the potential field function in the framework of optimal control theory, the desired yaw rate and the desired longitudinal deceleration are theoretically calculated. Finally, the validity of the proposed motion planning and control system is verified by comparing the simulation results with the actual driving data by experienced drivers.