# GenFollower: Enhancing Car-Following Prediction with Large Language Models

arXiv: https://arxiv.org/abs/2407.05611 · HTML: https://mlsanity.com/paper/2407.05611/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 8 Jul 2024.

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Language Prompt for Autonomous Driving](https://mlsanity.com/paper/2309.04379/) (2023)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models](https://mlsanity.com/paper/2403.18344/) (2024)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [A First Look at Ad-block Detection: A New Arms Race on the Web](https://mlsanity.com/paper/1605.05841/) (2016)
- [TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer](https://mlsanity.com/paper/2202.03183/) (2022)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
- [EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning](https://mlsanity.com/paper/2308.16008/) (2023)
- [MetaFollower: Adaptable Personalized Autonomous Car Following](https://mlsanity.com/paper/2406.16978/) (2024)
- [EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systems](https://mlsanity.com/paper/2407.02516/) (2024)
- [CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning](https://mlsanity.com/paper/2407.02517/) (2024)

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

- [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [DriveAgent: Multi-Agent Structured Reasoning with LLM and Multimodal Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2505.02123/) (2025)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models](https://mlsanity.com/paper/2504.14241/) (2025)
- [Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments](https://mlsanity.com/paper/2407.14247/) (2024)
- [EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption](https://mlsanity.com/paper/2408.03950/) (2024)
- [General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging](https://mlsanity.com/paper/2604.09609/) (2026)

## Abstract (arXiv)

Accurate modeling of car-following behaviors is essential for various applications in traffic management and autonomous driving systems. However, current approaches often suffer from limitations like high sensitivity to data quality and lack of interpretability. In this study, we propose GenFollower, a novel zero-shot prompting approach that leverages large language models (LLMs) to address these challenges. We reframe car-following behavior as a language modeling problem and integrate heterogeneous inputs into structured prompts for LLMs. This approach achieves improved prediction performance and interpretability compared to traditional baseline models. Experiments on the Waymo Open datasets demonstrate GenFollower's superior performance and ability to provide interpretable insights into factors influencing car-following behavior. This work contributes to advancing the understanding and prediction of car-following behaviors, paving the way for enhanced traffic management and autonomous driving systems.