# A Generative Car-following Model Conditioned On Driving Styles

arXiv: https://arxiv.org/abs/2112.05399 · HTML: https://mlsanity.com/paper/2112.05399/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018)
- [Attentive Neural Processes](https://mlsanity.com/paper/1901.05761/) (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)
- [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)
- [Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study](https://mlsanity.com/paper/1811.06395/) (2018)

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

- [Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems](https://mlsanity.com/paper/2308.07051/) (2023)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
- [Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks](https://mlsanity.com/paper/2405.11331/) (2024)
- [MetaFollower: Adaptable Personalized Autonomous Car Following](https://mlsanity.com/paper/2406.16978/) (2024)
- [A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research](https://mlsanity.com/paper/2410.07066/) (2024)
- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)
- [A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models](https://mlsanity.com/paper/2504.14241/) (2025)
- [Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control](https://mlsanity.com/paper/2502.00043/) (2025)

## Abstract (arXiv)

Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is able to generate realistic human CF behaviors for any given observed or even unobserved driving style. Specifically, the ability of accurately capturing human CF behaviors is ensured by designing and calibrating an Intelligent Driver Model (IDM) with time-varying parameters. The reason behind is that such time-varying parameters can express both the inter-driver heterogeneity, i.e., diverse driving styles of different drivers, and the intra-driver heterogeneity, i.e., changing driving styles of the same driver. The ability of generating realistic human CF behaviors of any given observed driving style is achieved by applying a neural process (NP) based model. The ability of inferring CF behaviors of unobserved driving styles is supported by exploring the relationship between the calibrated time-varying IDM parameters and an intermediate variable of NP. To demonstrate the effectiveness of our proposed models, we conduct extensive experiments and comparisons, including CF model parameter calibration, CF behavior prediction, and trajectory simulation for different driving styles.