mlsanityData to 7 Oct 2026

Imitating Driver Behavior with Generative Adversarial Networks

arXiv 1701.06699 ↗First posted 24 Jan 2017cs.AIcited by 92

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Cited by 34 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

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.

Key facts. Cited by 92 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2017.

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Citing papers per year: 2018: 3; 2019: 2; 2020: 6; 2021: 20; 2022: 17; 2023: 16; 2024: 15; 2025: 10; 2026: 3.

92 papers.

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  1. 2022BITS: Bi-level Imitation for Traffic Simulationcited by 80
  2. 2021Feedback in Imitation Learning: The Three Regimes of Covariate Shiftcited by 68
  3. 2022ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planningcited by 63
  4. 2021Trajectory Prediction with Latent Belief Energy-Based Modelcited by 60
  5. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  6. 2018Multi-Agent Imitation Learning for Driving Simulationcited by 49
  7. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 43
  8. 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overviewcited by 42
  9. 2024Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Followingcited by 41
  10. 2022Sequence Model Imitation Learning with Unobserved Contextscited by 34
  11. 2023Rethinking Closed-loop Training for Autonomous Drivingcited by 29
  12. 2021Social Coordination and Altruism in Autonomous Drivingcited by 28
38 more
  1. 2022Causal Imitation Learning under Temporally Correlated Noisecited by 28
  2. 2023BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Drivingcited by 28
  3. 2020On Computation and Generalization of Generative Adversarial Imitation Learningcited by 27
  4. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Drivingcited by 26
  5. 2018Learning from Demonstration in the Wildcited by 25
  6. 2019Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learningcited by 24
  7. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  8. 2021CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoningcited by 23
  9. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  10. 2020Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimizationcited by 22
  11. 2023EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Predictioncited by 21
  12. 2024Better than Your Teacher: LLM Agents that learn from Privileged AI Feedbackcited by 21
  13. 2020Inverse Reinforcement Learning with Natural Language Goalscited by 20
  14. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priorscited by 15
  15. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  16. 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approachcited by 13
  17. 2021Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximationcited by 11
  18. 2023Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environmentscited by 11
  19. 2024Intention-aware Denoising Diffusion Model for Trajectory Predictioncited by 11
  20. 2023Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Drivingcited by 9
  21. 2023A Bayesian Approach to Robust Inverse Reinforcement Learningcited by 9
  22. 2022Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehiclescited by 8
  23. 2023Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Predictioncited by 8
  24. 2024Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Drivingcited by 8
  25. 2020Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Ratecited by 7
  26. 2021Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarioscited by 7
  27. 2023Car-Following Models: A Multidisciplinary Reviewcited by 7
  28. 2025SPACeR: Self-Play Anchoring with Centralized Reference Modelscited by 7
  29. 2021Learning to Simulate on Sparse Trajectory Datacited by 6
  30. 2021A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filteringcited by 6
  31. 2022Imitation Learning by State-Only Distribution Matchingcited by 6
  32. 2022Moral Dilemmas for Moral Machinescited by 6
  33. 2023Causal Imitability Under Context-Specific Independence Relationscited by 6
  34. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environmentscited by 5
  35. 2022Trajectory Prediction for Vehicle Conflict Identification at Intersections Using Sequence-to-Sequence Recurrent Neural Networkscited by 5
  36. 2025Quantization-Free Autoregressive Action Transformercited by 5
  37. 2025DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learningcited by 5
  38. 2020Addressing reward bias in Adversarial Imitation Learning with neutral reward functionscited by 4

50 most cited of 92 All 92 (JSON)