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A cooperative collision-avoidance control methodology for virtual coupling trains.

Shuai Su1, Wentao Liu2, Qingyang Zhu2

  • 1State 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.

Accident; Analysis and Prevention
|May 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a cooperative collision-avoidance control for virtual coupled trains, significantly reducing following distances. The novel method ensures safety while enhancing rail transit efficiency.

Keywords:
Cooperative collision-avoidanceDQN algorithmRelative distance braking modeTrain operation safetyVirtual coupling

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Area of Science:

  • Rail Transit Engineering
  • Control Systems Theory
  • Artificial Intelligence in Transportation

Background:

  • Virtual coupling is a key technology for increasing rail transit capacity.
  • Safe and efficient train following control, particularly using relative distance braking mode (RDBM), is crucial.
  • Existing methods may limit operational efficiency while ensuring safety.

Purpose of the Study:

  • To propose an innovative cooperative collision-avoidance control methodology for virtual coupled trains.
  • To enhance operational efficiency and safety in rail transit.
  • To reduce the minimum following distance between trains.

Main Methods:

  • A novel framework for RDBM based on predicted train trajectories.
  • A cooperative control model formulated as a Markov decision process.
  • Application of the Deep-Q-Network (DQN) algorithm for learning control strategies.

Main Results:

  • The proposed approach significantly reduces the minimum following distance between trains by an average of 70.23% compared to absolute distance braking mode (ADBM).
  • Safety is maintained while improving operational efficiency.
  • Experimental simulations validate the effectiveness of the cooperative control methodology.

Conclusions:

  • The developed cooperative collision-avoidance control effectively enhances rail transit line transport capacity.
  • The integration of predicted trajectories and reinforcement learning (DQN) offers a robust solution for safe and efficient train following.
  • This methodology represents a significant advancement in virtual coupling control strategies.