用风险可达集结合强化学习，提升智能驾驶变道安全性和舒适性
Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR
- 构建融合驾驶风险场的可达集，精准识别可行驶区域风险
- 强化学习生成初始轨迹，再经iLQR优化实现安全高效路径
- 适合自动驾驶决策与高阶控制研究者参考
驾驶风险场适用于更复杂的驾驶场景，为复杂环境下的安全决策与主动车辆控制提供新思路。然而，现有研究常忽略驾驶风险场，未考虑可行驶区域内风险分布对轨迹规划的影响，制约了安全性提升。本文提出一种基于风险可达集的智能车辆轨迹规划方法，以进一步提高轨迹规划的安全性。首先，构建融合驾驶风险场的可达集，更准确评估并规避可行驶区域内的潜在风险；其次，基于安全强化学习生成初始轨迹，并将其投影至可达集；最后，引入基于约束迭代二次型调节器（iLQR）的轨迹规划方法，优化初始解，确保所规划轨迹在舒适性、安全性和效率上达到最优。在高速变道场景下进行仿真测试，结果表明，该方法能保证轨迹舒适与驾驶效率，生成轨迹位于高风险边界之外，保障运行过程中的车辆安全。
原文摘要 · Abstract (English)
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.
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