Deep reinforcement learning has gradually shown its latent decision-making ability in urban rail transit autonomous operation. However, since reinforcement learning can not neither guarantee safety during learning nor execution, this is still one of the major obstacles to the practical application of reinforcement learning. Given this drawback, reinforcement learning applied in the safety-critical autonomous operation domain remains challenging without generating a safe control command sequence that avoids overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper for safe intelligent control of urban rail transit autonomous operation trains. The proposed framework is combined with linear temporal logic, reinforcement learning and Monte Carlo tree search and consists of four mainly module: a post-posed shielding, a searching tree module, a DRL framework and an additional actor. Furthermore, the output of the framework can meet speed constraint, schedule constraint and optimize the operation process. Finally, the proposed SSA-DRL framework for decision-making in urban rail transit autonomous operation is evaluated in sixteen different sections, and its effectiveness is demonstrated through an ablation experiment and comparison with the scheduled operation plan.

提出了一种用于安全智能控制城市轨道交通自主运营列车的SSA-DRL框架，结合了线性时态逻辑、强化学习和蒙特卡洛树搜索，可以生成满足速度约束、时间表约束和优化运营流程的安全控制指令序列。通过消融实验和与计划操作方案的比较，评估了该框架在城市轨道交通自主运营中的决策效果。

如何确保安全的控制策略？走向城市公共交通自主运营的强化学习