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A Physics-Informed Deep Learning Paradigm for Car-Following Models

arXiv 2012.13376 ↗First posted 24 Dec 2020cs.LG eess.SPcited by 41

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Abstract (arXiv)

Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model. These models successfully interpret traffic phenomena observed in the real-world but may not fully capture the complex cognitive process of driving. Deep learning models, on the other hand, have demonstrated their power in capturing observed traffic phenomena but require a large amount of driving data to train. This paper aims to develop a family of neural network based car-following models that are informed by physics-based models, which leverage the advantage of both physics-based (being data-efficient and interpretable) and deep learning based (being generalizable) models. We design physics-informed deep learning car-following (PIDL-CF) architectures encoded with two popular physics-based models - IDM and OVM, on which acceleration is predicted for four traffic regimes: acceleration, deceleration, cruising, and emergency braking. Two types of PIDL-CFM problems are studied, one to predict acceleration only and the other to jointly predict acceleration and discover model parameters. We also demonstrate the superior performance of PIDL with the Next Generation SIMulation (NGSIM) dataset over baselines, especially when the training data is sparse. The results demonstrate the superior performance of neural networks informed by physics over those without. The developed PIDL-CF framework holds the potential for system identification of driving models and for the development of driving-based controls for automated vehicles.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Dec 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 10; 2024: 11; 2025: 11; 2026: 3.

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  1. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  2. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  3. 2021A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimationcited by 37
  4. 2021Applications of deep learning in traffic congestion detection, prediction and alleviation: A surveycited by 14
  5. 2023On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Modelscited by 14
  6. 2023Dynamic Hypergraph Structure Learning for Traffic Flow Forecastingcited by 14
  7. 2023Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problemscited by 13
  8. 2023Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlookcited by 11
  9. 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
  10. 2021A Generative Car-following Model Conditioned On Driving Stylescited by 8
  11. 2023Car-Following Models: A Multidisciplinary Reviewcited by 8
  12. 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learningcited by 8
29 more
  1. 2022Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networkscited by 7
  2. 2024Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory controlcited by 7
  3. 2023A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Predictioncited by 5
  4. 2024MetaFollower: Adaptable Personalized Autonomous Car Followingcited by 4
  5. 2024EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systemscited by 4
  6. 2025Position: Biology is the Challenge Physics-Informed ML Needs to Evolvecited by 4
  7. 2022IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence Car-Following Trajectory Predictioncited by 3
  8. 2025Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behaviorcited by 3
  9. 2023Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approachcited by 2
  10. 2024Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approachcited by 2
  11. 2024COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecastingcited by 2
  12. 2024PITA: Physics-Informed Trajectory Autoencodercited by 2
  13. 2024Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Controlcited by 2
  14. 2025Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysiscited by 2
  15. 2023Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flowcited by 1
  16. 2023TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Populationcited by 1
  17. 2023RACER: Rational Artificial Intelligence Car-following-model Enhanced by Realitycited by 1
  18. 2024Discovering Car-following Dynamics from Trajectory Data through Deep Learningcited by 1
  19. 2025A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Modelscited by 1
  20. 2025Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Controlcited by 0
  21. 2025Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approachcited by 0
  22. 2025Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approachcited by 0
  23. 2025Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Modelscited by 0
  24. 2025Theory Foundation of Physics-Enhanced Residual Learningcited by 0
  25. 2025A phase-aware AI car-following model for electric vehicles with adaptive cruise control: Development and validation using real-world datacited by 0
  26. 2025Learn2Drive: A neural network-based framework for socially compliant automated vehicle controlcited by 0
  27. 2026Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportationcited by 0
  28. 2026Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Trafficcited by 0
  29. 2026REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Modelscited by 0