This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.
MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors
IEEE Transactions on Intelligent Transportation Systems ; 20 , 2 ; 790-796
2019-02-01
2447071 byte
Article (Journal)
Electronic Resource
English
DOAJ | 2018
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