Research on Active Obstacle Avoidance for Autonomous Vehicle Based on Model Predictive Control

Abstract

In order to track the active obstacle avoidance path of the autonomous vehicle, this thesis proposes two controllers with different inputs and constraints, on the basis of the Model Predictive Control (MPC). The controller A takes front-wheel steering angle as control input, while the controller B adds rear-wheel braking torque as another control input with extra constraints on the rear wheel braking torque. The simulation results indicate that both controllers can well track the desired path. More specifically, the performance of controller B is better than controller A in terms of lateral displacement and yaw rate, but the performance of controller A is better in yaw angle.