A cooperative collision-avoidance control methodology for virtual coupling trains. (August 2022)
- Record Type:
- Journal Article
- Title:
- A cooperative collision-avoidance control methodology for virtual coupling trains. (August 2022)
- Main Title:
- A cooperative collision-avoidance control methodology for virtual coupling trains
- Authors:
- Su, Shuai
Liu, Wentao
Zhu, Qingyang
Li, Ruoqing
Tang, Tao
Lv, Jidong - Abstract:
- Highlights: A novel framework for the RDBM is proposed based on the predicted operation trajectory of the preceding train. A cooperative collision-avoidance control methodology is proposed to ensure the safety and enhance the operation efficiency. The DQN algorithm is introduced to learn the safe and efficient control strategy. The effectiveness of the proposed approach is verified by experimental simulations Abstract: 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 illustrateHighlights: A novel framework for the RDBM is proposed based on the predicted operation trajectory of the preceding train. A cooperative collision-avoidance control methodology is proposed to ensure the safety and enhance the operation efficiency. The DQN algorithm is introduced to learn the safe and efficient control strategy. The effectiveness of the proposed approach is verified by experimental simulations Abstract: 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. … (more)
- Is Part Of:
- Accident analysis and prevention. Volume 173(2022)
- Journal:
- Accident analysis and prevention
- Issue:
- Volume 173(2022)
- Issue Display:
- Volume 173, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 173
- Issue:
- 2022
- Issue Sort Value:
- 2022-0173-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-08
- Subjects:
- Virtual coupling -- Train operation safety -- Cooperative collision-avoidance -- Relative distance braking mode -- DQN algorithm
Accidents -- Prevention -- Periodicals
Accident Prevention -- Periodicals
Accidents -- Prévention -- Périodiques
363.106 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00014575 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aap.2022.106703 ↗
- Languages:
- English
- ISSNs:
- 0001-4575
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0573.130000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21788.xml