An Adaptive Motion Planning Technique for On-Road Autonomous Driving

This paper presents a hierarchical motion planning approach based on discrete optimization method. Well-coupled longitudinal and lateral planning strategies with adaptability features are applied for better performance of on-road autonomous driving with avoidance of both static and moving obstacles. In the path planning level, the proposed method starts with a speed profile designing for the determination of longitudinal horizon, then a set of candidate paths will be constructed with lateral offsets shifting from the base reference. Cost functions considering driving comfort and energy consumption are applied to evaluate each candidate path and the optimal one will be selected as tracking reference afterwards. Re-determination of longitudinal horizon in terms of relative distance between ego vehicle and surrounding obstacles, together with update of speed profile, will be triggered for re-planning if candidate paths ahead fail the safety checking. In the path tracking level, a pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of autonomous vehicle. Simulation results demonstrate the effectiveness of the proposed method and indicate its better performance in extreme traffic scenarios compared to traditional discrete optimization methods, while balancing computational burden at the same time.

Automated summary

This paper presents a hierarchical motion planning approach based on discrete optimization method for on-road autonomous driving. It uses well-coupled longitudinal and lateral planning strategies to achieve better performance, with re-determination of longitudinal horizon and update of speed profile for re-planning if candidate paths ahead fail the safety checking. A pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of the autonomous vehicle.