On-Road Trajectory Planning of Connected and Automated Vehicles in Complex Traffic Settings: A Hierarchical Framework of Trajectory Refinement

Abstract

This paper presents a hierarchical framework for on-road trajectory planning in complex traffic environments. Firstly, the processing of sparse coarse trajectories involves the utilization of DP (Dynamic Programming) generation and interpolation techniques. Then, for the waypoints with collision risk in the smoothed trajectory, the spiral search method is used to find some safe alternate waypoints. The alternate waypoints and the previous ones without collision risk form the amended trajectory. Concurrently, safety tunnels are constructed along the amended trajectory for the ego vehicle. Furthermore, with the constraint conditions of vehicle kinematics model and safety tunnels, nonlinear program (NLP) optimization is carried out for the amended trajectory of ego vehicle to obtain smooth and safe trajectories. For typical cases, simulation experiments demonstrate that the ego vehicle can ensure collision safety in dynamic traffic scenarios, while maintaining smooth vehicle velocity and small jitter of the front wheel angle. The proposed trajectory planning framework provides a novel decision-making method for connected and automated vehicles (CAVs).