# Car-Following Models: A Multidisciplinary Review

arXiv: https://arxiv.org/abs/2304.07143 · HTML: https://mlsanity.com/paper/2304.07143/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 303 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Imitating Driver Behavior with Generative Adversarial Networks](https://mlsanity.com/paper/1701.06699/) (2017)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [Limitations and Improvements of the Intelligent Driver Model (IDM)](https://mlsanity.com/paper/2104.02583/) (2021)
- [Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections](https://mlsanity.com/paper/2105.01357/) (2021)
- [openACC. An open database of car-following experiments to study the properties of commercial ACC systems](https://mlsanity.com/paper/2004.06342/) (2020)
- [A Generative Car-following Model Conditioned On Driving Styles](https://mlsanity.com/paper/2112.05399/) (2021)
- [Platoon trajectories generation: A unidirectional interconnected LSTM-based car following model](https://mlsanity.com/paper/1910.11843/) (2019)
- [Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach](https://mlsanity.com/paper/2103.15047/) (2021)
- [Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments](https://mlsanity.com/paper/2302.07453/) (2023)
- [Empirical Study of DSRC Performance Based on Safety Pilot Model Deployment Data](https://mlsanity.com/paper/1606.08365/) (2016)
- [CAN Coach: Vehicular Control through Human Cyber-Physical Systems](https://mlsanity.com/paper/2104.06264/) (2021)
- [Offline reconstruction of missing vehicle trajectory data from 3D LIDAR](https://mlsanity.com/paper/1709.05446/) (2017)
- [Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video](https://mlsanity.com/paper/2209.08417/) (2022)

## Built on by (most cited first, 8 of 8)

- [GenFollower: Enhancing Car-Following Prediction with Large Language Models](https://mlsanity.com/paper/2407.05611/) (2024)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)
- [Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors](https://mlsanity.com/paper/2407.02546/) (2024)
- [Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control](https://mlsanity.com/paper/2502.00043/) (2025)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)
- [Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks](https://mlsanity.com/paper/2506.13950/) (2025)
- [Calibration and Evaluation of Car-Following Models for Autonomous Shuttles Using a Novel Multi-Criteria Framework](https://mlsanity.com/paper/2602.11517/) (2026)

## Abstract (arXiv)

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help us understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-following models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, and reinforcement learning. This paper presents an extensive survey that highlights the differences, complementarities, and overlaps among microscopic traffic flow and control models based on their underlying principles and design logic. It reviews representative algorithms, ranging from theory-based kinematic models, Psycho-Physical Models, and Adaptive cruise control models to data-driven algorithms like Reinforcement Learning (RL) and Imitation Learning (IL). The manuscript discusses the strengths and limitations of these models and explores their applications in different contexts. This review synthesizes existing researches across different domains to fill knowledge gaps and offer guidance for future research by identifying the latest trends in car following models and their applications.