Hierarchical Motion Planning and Control Algorithm of Autonomous Racing Vehicles for Overtaking Maneuvers

This paper describes a hierarchical motion planning and control framework for overtaking maneuvers under racing circumstances. Unlike urban or highway autonomous driving conditions, race track driving requires longer prediction and planning horizons in order to respond to upcoming corners at high speed. In addition, the subject vehicle should determine the optimal action among possible driving modes when opponent vehicles are present. In order to meet these requirements and secure real time performance, a hierarchical architecture for decision making, motion planning, and control for an autonomous racing vehicle is proposed. The supervisor determines whether the subject vehicle should stay behind the preceding vehicle or overtake, and its direction when overtaking. Next, a high level trajectory planner generates the desired path and velocity profile in a receding horizon fashion. In order to reduce the computational burden despite maintaining a sufficiently long planning horizon, a low fidelity kinematic vehicle model is utilized for the planner. Finally, a model predictive control (MPC) based low level trajectory tracker generates the lateral and longitudinal control inputs. A high fidelity dynamic vehicle model is introduced to provide a high quality control input for a relatively short control horizon compared with the motion planner. Simulation results demonstrate that the proposed algorithm successfully controls the subject vehicle to deal with preceding opponent vehicles under racing track environments.