A Holistic Safe Planner for Automated Driving Considering Interaction With Human Drivers.

This paper advances state-of-the-art automated driving systems with a comprehensive framework that encompasses decision making, maneuver planning, and trajectory tracking considering safety, computational efficiency, and passenger comfort. In face of the co-existence of automated vehicles (AVs) and human-driven vehicles (HDVs), a decision making framework of AVs is proposed for safe lane keeping or changing. The decision making is based on the HDVs' future motion predicted by a learning-based Long Short-Term Memory model. To quantify the uncertainties in prediction, an error ellipse is used to capture the model deviations from the ground truth to ensure driving safety. This paper develops a novel method that leverages lower-order parametric curves to efficiently generate feasible, safe, and comfortable lateral movements for AVs. The planner is complemented by maneuver replanning that can guide the AV back to the original lane when confronted with unexpected blockages from surrounding vehicles. Based on real-world datasets, simulation results show that the proposed method achieves curvature compatibility, shorter trajectory length in lateral maneuvers, accurate trajectory tracking, and effective collision avoidance in lane changing.