Obstacle avoidance of autonomous vehicles based on model predictive control

This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified model and the actual vehicle, a separate controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle are controlled using the inverse dynamics of the vehicle powertrain model, and the lateral dynamics are controlled using a linear quadratic regulator. In the non-linear model predictive framework, to obtain safe trajectories, local obstacle information is incorporated into the performance index using a parallax-based method. Simulation results on a full non-linear vehicle model show that the proposed combination of model-predictive-control-based trajectory generation and tracking controller gives satisfactory online obstacle avoidance performance.