A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
This work addresses the problem of real-time, safe decision-making for autonomous vehicles, though it appears incremental as it builds on existing hierarchical and reinforcement learning approaches.
The paper tackles the challenge of generating safe and efficient trajectories for autonomous vehicles in complex, interactive traffic scenarios by proposing a hierarchical planning framework (H-CtRL) that combines low-level safe controllers with a high-level reinforcement learning coordinator, achieving satisfying performance in safety and efficiency in realistic simulations.
Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based motion planner cannot efficiently generate safe trajectories in real time, particularly when there are many interactive vehicles near by. On the other hand, end-to-end learning methods cannot assure the safety of the outcomes. To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers. Safety is guaranteed by the low-level optimization/sampling-based controllers, while the high-level reinforcement learning algorithm makes H-CtRL an adaptive and efficient behavior planner. To train and test our proposed algorithm, we built a simulator that can reproduce traffic scenes using real-world datasets. The proposed H-CtRL is proved to be effective in various realistic simulation scenarios, with satisfying performance in terms of both safety and efficiency.