Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control

Abstract

Trajectory planning and tracking control are two keys of collision avoidance for autonomous vehicles in critical traffic scenarios. It requires not only the system functionality, but also strong real-time. In this paper, we integrated trajectory planner and tracking controller for autonomous vehicle to implement trace planning and tracking for obstacle avoidance. The trajectory planner is based on the state lattice approach and the tracking controller is designed based on the model predictive control using the vehicle kinematics model. The simulation shows that the planner can generate smooth trajectories which could be selected as references for the controller. The maximum tracking error is less than 0.2 m when the vehicle speed is below 50 km/h. Additionally, the on field test shows that the test vehicle with this method is capable of following the reference path accurately, even at sharp corners.