A cooperative collision-avoidance control methodology for virtual coupling trains

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.

Automated summary

This paper proposes a train collision-avoidance control strategy based on predicted trajectory, which is achieved through the introduction of a cooperative control model and the Deep-Q-Network algorithm. Experimental simulations demonstrate that the proposed approach can significantly reduce the minimum following distance between adjacent trains compared to the absolute distance braking mode, while ensuring safety.