An Empirical Markov Chain Car-Following (MC-CF) Model
Car-following behavior is fundamental to traffic flow theory, yet traditional models often fail to capture the stochasticity of naturalistic driving. This paper proposes an empirical probabilistic sampling approach to car-following modeling that bypasses conventional parametric assumptions. Under this approach, we introduce the Markov Chain Car-Following (MC-CF) model, which represents state transitions as a Markov process and predicts behavior by randomly sampling accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset (WOMD) demonstrates that MC-CF variants significantly outperform all physics-based baselines (IDM, Gipps, FVD...
| Authors | Sungyong Chung, Yanlin Zhang, Nachuan Li et al. |
|---|---|
| Primary category | eess.SY |
| Published | 29 March 2026 |
| Last updated at source | 11 September 2026 |
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