  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.
