State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Accid Anal Prev. 2022 Aug;173:106703. doi: 10.1016/j.aap.2022.106703. Epub 2022 May 15.
To further improve the line transport capacity, virtual coupling has become a frontier hot topic in the field of rail transit. Specially, the safe and efficient following control strategy based on relative distance braking mode (RDBM) is one of the core technologies. This paper innovatively proposes a cooperative collision-avoidance control methodology, which can enhance the operation efficiency on the premise of ensuring the safety. Firstly, a novel framework for the RDBM based on the predicted trajectory of the preceding train is proposed for the train collision-avoidance control. To reduce the train following distance, a cooperative control model is further proposed and is formulated as a Markov decision process. Then, the Deep-Q-Network (DQN) algorithm is introduced to solve the efficient control problem by learning the safe and efficient control strategy for the following train where the critical elements of the reinforcement learning framework are designed. Finally, experimental simulations are conducted based on the simulated environment to illustrate the effectiveness of the proposed approach. Compared with the absolute distance braking mode (ADBM), the minimum following distance between the adjacent trains can be reduced by 70.23% on average via the proposed approach while the safety can be guaranteed.
为进一步提高线路运输能力，虚拟耦合已成为轨道交通领域的前沿热点问题。特别是，基于相对距离制动模式（RDBM）的安全高效的追踪控制策略是核心技术之一。本文创新性地提出了一种协同防撞控制方法，可以在保证安全的前提下提高运行效率。首先，为列车防撞控制提出了一种基于前车预测轨迹的新型 RDBM 框架。为了减少列车追踪间隔，进一步提出了协同控制模型，并将其表述为马尔可夫决策过程。然后，引入深度 Q 网络（DQN）算法，通过学习后续列车的安全高效控制策略，解决高效控制问题，其中设计了强化学习框架的关键要素。最后，基于仿真环境进行了实验仿真，验证了所提方法的有效性。与绝对距离制动模式（ADBM）相比，所提方法可使相邻列车之间的最小追踪间隔平均减少 70.23%，同时保证安全性。
