mlsanityData to 14 Oct 2026

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

arXiv 1811.06395 ↗First posted 11 Nov 2018cs.RO cs.AIcited by 11

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Abstract (arXiv)

Five car-following models were calibrated, validated and cross-compared. The intelligent driver model performed best among the evaluated models. Considerable behavioral differences between different drivers were found. Calibrated model parameters may not be numerically equivalent with observed ones.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2018.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 4; 2025: 1.

11 papers.

  1. 2023FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modelingcited by 21
  2. 2022TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformercited by 9
  3. 2023EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learningcited by 9
  4. 2021A Generative Car-following Model Conditioned On Driving Stylescited by 8
  5. 2024Automating Traffic Model Enhancement with AI Research Agentcited by 8
  6. 2024CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Controlcited by 7
  7. 2023EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planningcited by 6
  8. 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
  9. 2024MetaFollower: Adaptable Personalized Autonomous Car Followingcited by 4
  10. 2024Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regressioncited by 2
  11. 2021Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Modelcited by 0