The virtually coupled trains present challenges due to the risks associated with small separation. The following train must make appropriate decisions in uncertain and high-risk situations to ensure safety and efficiency. We address uncertainty from sensor noise and unknown preceding train behaviors, establishing a probabilistic decision-making model using a partially observable Markov decision process (POMDP). Collision and risk envelopes are defined using the emergency and full-service braking distance of the following train, considering the “worst-case separation” scenarios, leading to a novel chance-constrained POMDP (CC-POMDP) for the virtually coupled trains. We propose an online planning algorithm, Partially Observable Monte Carlo Planning based on Double Shielding and Progressive Widening (POMCP-DS-PW). The results demonstrate that even without a pre-planned recommended speed curve, the following train can optimize acceleration based on incomplete observations and unknown behaviors of the preceding train, thereby ensuring safety while achieving operational efficiency.


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    Title :

    Probabilistic Decision-Making for Virtually Coupled Trains Under Uncertainty


    Contributors:
    Wu, Yiling (author) / Yan, Fei (author)


    Publication date :

    2025-06-01


    Size :

    4784915 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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