A discrete-time linear model predictive control for motion planning of an autonomous vehicle with adaptive cruise control and obstacle overtaking

Motion planners for autonomous driving improve traffic safety through collision-free motion generation along the path. However, conventional motion planners render passengers uncomfortable as a result of jerky motion. To overcome this, we propose a model predictive control (MPC) based motion planner that not only ensures safety but also improves driving comfort. The proposed planner generates path tracking and collision-free maneuvers to ensure safety, and improve driving comfort by minimizing acceleration and jerk. Collision-free maneuvers include vehicle following and overtaking. The target speed is determined by comprehensively considering path tracking performance improvement and whether overtaking is possible. The speed of the vehicle is controlled by considering longitudinal acceleration and jerk minimization. The steering command is determined by considering both path tracking error reduction, and lateral acceleration and jerk minimization. In cases where vehicle overtaking is required and during high-speed driving conditions, the consideration of lateral acceleration and jerk minimization increases to improve driving comfort. The proposed planner is formulated as a convex optimization problem. The effectiveness of the proposed planner was evaluated in path tracking and collision avoidance simulations. The simulation results confirm that the proposed planner ensures vehicle safety through lane keeping and collision avoidance, and improves driving comfort.