mlsanityData to 7 Oct 2026

Exploring the Limitations of Behavior Cloning for Autonomous Driving

arXiv 1904.08980 ↗First posted 18 Apr 2019cs.CV cs.AIcited by 343

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Cited by 177 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https://github.com/felipecode/coiltraine .

Key facts. Cited by 343 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 3; 2020: 7; 2021: 40; 2022: 52; 2023: 69; 2024: 66; 2025: 61; 2026: 45.

343 papers.

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  1. 2022Planning-oriented Autonomous Drivingcited by 1,068
  2. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  3. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  4. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 482
  5. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  6. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  7. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  8. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  9. 2023Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Drivingcited by 309
  10. 2023Tool Learning with Foundation Modelscited by 302
  11. 2022Behavior Transformers: Cloning k modes with one stonecited by 299
  12. 2024VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planningcited by 287
38 more
  1. 2019Causal Confusion in Imitation Learningcited by 286
  2. 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 258
  3. 2024Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Drivingcited by 250
  4. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  5. 2024SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representationcited by 239
  6. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  7. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
  8. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  9. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Modelscited by 204
  10. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  11. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  12. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  13. 2024Enhancing End-to-End Autonomous Driving with Latent World Modelcited by 161
  14. 2024Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Drivingcited by 156
  15. 2022Model-Based Imitation Learning for Urban Drivingcited by 149
  16. 2023DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Drivingcited by 142
  17. 2025Pseudo-Simulation for Autonomous Drivingcited by 141
  18. 2022VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priorscited by 135
  19. 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Drivingcited by 125
  20. 2023Dolphins: Multimodal Language Model for Drivingcited by 124
  21. 2025GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Drivingcited by 117
  22. 2021Learning to drive from a world on railscited by 114
  23. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  24. 2023Policy Expansion for Bridging Offline-to-Online Reinforcement Learningcited by 103
  25. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  26. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  27. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  28. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  29. 2022COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehiclescited by 95
  30. 2023Hidden Biases of End-to-End Driving Modelscited by 95
  31. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 92
  32. 2024VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervisioncited by 85
  33. 2019Urban Driving with Conditional Imitation Learningcited by 84
  34. 2024PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Drivingcited by 84
  35. 2024HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Drivingcited by 84
  36. 2025Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Drivingcited by 84
  37. 2022Differentiable Raycasting for Self-supervised Occupancy Forecastingcited by 83
  38. 2024Diffusion Actor-Critic with Entropy Regulatorcited by 81

50 most cited of 343 All 343 (JSON)

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  1. 2020Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
  2. 2026Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
  3. 2026Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving
  4. 2026Imitation Learning for Autonomous Driving in CARLA
  5. 2024Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
  6. 2021Iterative Imitation Policy Improvement for Interactive Autonomous Driving
  7. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
  8. 2025Post-Training and Test-Time Scaling of Generative Agent Behavior Models for Interactive Autonomous Driving