Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR

The driving risk field is applicable to more complex driving scenarios, providing new approaches for safety decision-making and active vehicle control in intricate environments. However, existing research often overlooks the driving risk field and fails to consider the impact of risk distribution within drivable areas on trajectory planning, which poses challenges for enhancing safety. This paper proposes a trajectory planning method for intelligent vehicles based on the risk reachable set to further improve the safety of trajectory planning. First, we construct the reachable set incorporating the driving risk field to more accurately assess and avoid potential risks in drivable areas. Then, the initial trajectory is generated based on safe reinforcement learning and projected onto the reachable set. Finally, we introduce a trajectory planning method based on a constrained iterative quadratic regulator to optimize the initial solution, ensuring that the planned trajectory achieves optimal comfort, safety, and efficiency. We conduct simulation tests of trajectory planning in high-speed lane-changing scenarios. The results indicate that the proposed method can guarantee trajectory comfort and driving efficiency, with the generated trajectory situated outside high-risk boundaries, thereby ensuring vehicle safety during operation.

@article{huang2025_2503.17398,
  title={ Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR },
  author={ Wenjie Huang and Yang Li and Shijie Yuan and Jingjia Teng and Hongmao Qin and Yougang Bian },
  journal={arXiv preprint arXiv:2503.17398},
  year={ 2025 }
}