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.
Probabilistic Decision-Making for Virtually Coupled Trains Under Uncertainty
IEEE Transactions on Intelligent Transportation Systems ; 26 , 6 ; 8602-8611
01.06.2025
4784915 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Robust Adaptive Control for Virtually Coupled Trains
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