# When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior

arXiv: https://arxiv.org/abs/2507.07012 · HTML: https://mlsanity.com/paper/2507.07012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 9 Jul 2025.

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

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [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)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios](https://mlsanity.com/paper/2003.00759/) (2020)
- [A Generative Car-following Model Conditioned On Driving Styles](https://mlsanity.com/paper/2112.05399/) (2021)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
- [Platoon trajectories generation: A unidirectional interconnected LSTM-based car following model](https://mlsanity.com/paper/1910.11843/) (2019)
- [Better Batch for Deep Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2305.17028/) (2023)
- [IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence Car-Following Trajectory Prediction](https://mlsanity.com/paper/2210.10965/) (2022)
- [Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior](https://mlsanity.com/paper/2506.14762/) (2025)
- [Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems](https://mlsanity.com/paper/2107.07832/) (2021)
- [Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression](https://mlsanity.com/paper/2404.16023/) (2024)
- [A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions](https://mlsanity.com/paper/2505.06680/) (2025)

## Built on by (most cited first, 2 of 2)

- [Behavioral Heterogeneity as Quantum-Inspired Representation](https://mlsanity.com/paper/2603.22729/) (2026)
- [Quantum-Inspired Modeling of Driving Behavior](https://mlsanity.com/paper/2608.25907/) (2026)

## Abstract (arXiv)

Modeling car-following behavior is fundamental to microscopic traffic simulation, yet traditional deterministic models often fail to capture the full extent of variability and unpredictability in human driving. While many modern approaches incorporate context-aware inputs (e.g., spacing, speed, relative speed), they frequently overlook structured stochasticity that arises from latent driver intentions, perception errors, and memory effects -- factors that are not directly observable from context alone. To fill the gap, this study introduces an interpretable stochastic modeling framework that captures not only context-dependent dynamics but also residual variability beyond what context can explain. Leveraging deep neural networks integrated with nonstationary Gaussian processes (GPs), our model employs a scenario-adaptive Gibbs kernel to learn dynamic temporal correlations in acceleration decisions, where the strength and duration of correlations between acceleration decisions evolve with the driving context. This formulation enables a principled, data-driven quantification of uncertainty in acceleration, speed, and spacing, grounded in both observable context and latent behavioral variability. Comprehensive experiments on the naturalistic vehicle trajectory dataset collected from the German highway, i.e., the HighD dataset, demonstrate that the proposed stochastic simulation method within this framework surpasses conventional methods in both predictive performance and interpretable uncertainty quantification. The integration of interpretability and accuracy makes this framework a promising tool for traffic analysis and safety-critical applications.