# MetaFollower: Adaptable Personalized Autonomous Car Following

arXiv: https://arxiv.org/abs/2406.16978 · HTML: https://mlsanity.com/paper/2406.16978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Modular meta-learning](https://mlsanity.com/paper/1806.10166/) (2018)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches](https://mlsanity.com/paper/1708.08986/) (2017)
- [Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study](https://mlsanity.com/paper/1811.06395/) (2018)
- [A Generative Car-following Model Conditioned On Driving Styles](https://mlsanity.com/paper/2112.05399/) (2021)
- [TrafficNet: An Open Naturalistic Driving Scenario Library](https://mlsanity.com/paper/1708.01872/) (2017)
- [Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles](https://mlsanity.com/paper/2008.12451/) (2020)
- [Driver Identification through Stochastic Multi-State Car-Following Modeling](https://mlsanity.com/paper/2005.11077/) (2020)

## Built on by (most cited first, 4 of 4)

- [GenFollower: Enhancing Car-Following Prediction with Large Language Models](https://mlsanity.com/paper/2407.05611/) (2024)
- [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026)
- [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)

## Abstract (arXiv)

Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing interest of researchers in the past decades. In this study, we propose an adaptable personalized car-following framework -MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. Unlike conventional adaptive cruise control (ACC) systems that rely on predefined settings and constant parameters without considering heterogeneous driving characteristics, MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning.