Dynamic motion planner with trajectory optimisation for automated highway lanechanging driving

Abstract

This study proposes a dynamic motion planner with trajectory optimisation for automated highway lane-changing driving. Owing to the connected and automated vehicles (CAVs) technology that the real-time traffic information can be obtained, alternative trajectories can be generated to satisfy the vehicle kinematic constraints and avoid many types of potential collisions. An optimal control theory is adopted to select an optimal lane-changing path from the finite path set, and the appropriate acceleration and speed for the execution path are also determined. In order to avoid unnecessary motion replanning process, this study puts forward a collision-avoidance monitoring algorithm to reduce the time consumption costs of the motion planner. Moreover, an online planning framework based on ‘decision-execution’ is explored. Applying this timeline framework can not only help to evaluate the dynamic planner’s online performance, but also reduce the deviation between the online calculation and the actual execution caused by the time consumption. The simulations are performed in PreScan- Simulink platform and the experimental results show that the presented dynamic planner can complete the lane-changing manoeuvre safely and effectively in a high-speed environment.