# Imitating Driver Behavior with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1701.06699 · HTML: https://mlsanity.com/paper/1701.06699/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2017.

## Built on by (most cited first, 30 of 93)

- [BITS: Bi-level Imitation for Traffic Simulation](https://mlsanity.com/paper/2208.12403/) (2022)
- [Feedback in Imitation Learning: The Three Regimes of Covariate Shift](https://mlsanity.com/paper/2102.02872/) (2021)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Trajectory Prediction with Latent Belief Energy-Based Model](https://mlsanity.com/paper/2104.03086/) (2021)
- [Multi-Agent Imitation Learning for Driving Simulation](https://mlsanity.com/paper/1803.01044/) (2018)
- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview](https://mlsanity.com/paper/2110.00675/) (2021)
- [Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following](https://mlsanity.com/paper/2402.06559/) (2024)
- [Sequence Model Imitation Learning with Unobserved Contexts](https://mlsanity.com/paper/2208.02225/) (2022)
- [Rethinking Closed-loop Training for Autonomous Driving](https://mlsanity.com/paper/2306.15713/) (2023)
- [On Computation and Generalization of Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2001.02792/) (2020)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Causal Imitation Learning under Temporally Correlated Noise](https://mlsanity.com/paper/2202.01312/) (2022)
- [BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2312.06371/) (2023)
- [Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving](https://mlsanity.com/paper/2102.09243/) (2021)
- [Learning from Demonstration in the Wild](https://mlsanity.com/paper/1811.03516/) (2018)
- [Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning](https://mlsanity.com/paper/1903.05766/) (2019)
- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback](https://mlsanity.com/paper/2410.05434/) (2024)
- [CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning](https://mlsanity.com/paper/2106.13364/) (2021)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization](https://mlsanity.com/paper/2006.13258/) (2020)
- [EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction](https://mlsanity.com/paper/2308.06564/) (2023)
- [Inverse Reinforcement Learning with Natural Language Goals](https://mlsanity.com/paper/2008.06924/) (2020)
- [Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors](https://mlsanity.com/paper/2408.03200/) (2024)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach](https://mlsanity.com/paper/2102.12668/) (2021)
- [Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments](https://mlsanity.com/paper/2302.04823/) (2023)
- [Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation](https://mlsanity.com/paper/2108.08765/) (2021)

## Abstract (arXiv)

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.