MOHA

A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

Journal article (2019)

Authors

Q. Lin Cyber Security - EEMCS
Yihuan Zhang Tongji University
S.E. Verwer Cyber Security - EEMCS
Jun Wang Tongji University
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2019 Q. Lin, Yihuan Zhang, S.E. Verwer, Jun Wang
DOI
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https://doi.org/10.1109/TITS.2018.2823418
Data mining Numerical models Time series analysis Vehicles Car-following behavior Computational modeling Data models Hybrid automaton Learning automata Simulation and control.
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

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.