Safe planning and control under uncertainty for self-driving
This addresses safety challenges in autonomous driving by reducing over-conservatism while maintaining robustness, though it appears incremental as it builds on existing planning approaches.
The paper tackles motion planning under uncertainty for self-driving vehicles by proposing a unified obstacle avoidance framework that handles ego-vehicle motion uncertainty and dynamic obstacle prediction uncertainty, demonstrating effectiveness, safety, and real-time performance in the CARLA simulator.
Motion Planning under uncertainty is critical for safe self-driving. In this paper, we propose a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle motion; and 2) prediction uncertainty of dynamic obstacles from environment. A two-stage traffic participant trajectory predictor comprising short-term and long-term prediction is used in the planning layer to generate safe but not over-conservative trajectories for the ego vehicle. The prediction module cooperates well with existing planning approaches. Our work showcases its effectiveness in a Frenet frame planner. A robust controller using tube MPC guarantees safe execution of the trajectory in the presence of state noise and dynamic model uncertainty. A Gaussian process regression model is used for online identification of the uncertainty's bound. We demonstrate effectiveness, safety, and real-time performance of our framework in the CARLA simulator.