Optimization-free trajectory planning for highly automated vehicles in highly dynamic environments

Real-time trajectory planning in highly dynamic poses high challenges for the safe performance of highly automated vehicles, especially when considering the limited computational resources. In this paper, we propose a novel optimization-free trajectory planning approach for highly automated vehicles that integrates Bézier curve-based path planning with constrained backstepping-control-based velocity planning. Firstly, by leveraging the geometric properties of Bézier curves, we ensure several smooth and static-obstacle-collision-free paths. Subsequently, we implement backstepping control to plan the velocity considering states and control input constraints for selected paths. Finally, the planned trajectories are evaluated, and the optimal one is chosen based on predefined rules. This optimization-free property makes the proposed method update the trajectory at high frequencies, improving the ability to deal with highly dynamic environments. Simulation results based on Carsim demonstrate the effectiveness of the proposed method.