Task-Motion Planning for Safe and Efficient Urban Driving
This addresses the critical challenge of safe and efficient autonomous driving in urban environments, representing an incremental improvement by integrating safety communication into existing planning frameworks.
The paper tackles the problem of autonomous vehicles needing to simultaneously maximize task-level efficiency and ensure motion-level safety in urban driving. It introduces TMPUD, which enables communication between task and motion planners about safety levels, and shows it significantly outperforms baselines in efficiency while maintaining safety in realistic simulations.
Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.