mlsanityData to 7 Oct 2026

End-to-end Driving via Conditional Imitation Learning

arXiv 1710.02410 ↗First posted 6 Oct 2017cs.RO cs.CV cs.LGcited by 479

Not on the map. Why?

Cited by 201 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at https://youtu.be/cFtnflNe5fM

Key facts. Cited by 479 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2017.

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Citing papers per year: 2018: 1; 2019: 9; 2020: 10; 2021: 92; 2022: 67; 2023: 76; 2024: 72; 2025: 78; 2026: 74.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2022Planning-oriented Autonomous Drivingcited by 1,068
  3. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  4. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  5. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  6. 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 338
  7. 2024Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Drivingcited by 250
  8. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  9. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  10. 2024SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representationcited by 239
  11. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  12. 2024EMMA: End-to-End Multimodal Model for Autonomous Drivingcited by 226
38 more
  1. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
  2. 2023ViNT: A Foundation Model for Visual Navigationcited by 210
  3. 2024NaVILA: Legged Robot Vision-Language-Action Model for Navigationcited by 201
  4. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
  5. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  6. 2021RvS: What is Essential for Offline RL via Supervised Learning?cited by 184
  7. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  8. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  9. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158
  10. 2022Model-Based Imitation Learning for Urban Drivingcited by 149
  11. 2023DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Drivingcited by 142
  12. 2020Language-Conditioned Imitation Learning for Robot Manipulation Taskscited by 136
  13. 2022Learning from All Vehiclescited by 135
  14. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
  15. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  16. 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Drivingcited by 125
  17. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
  18. 2025GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Drivingcited by 117
  19. 2021Learning to drive from a world on railscited by 114
  20. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  21. 2022Navigating to Objects in the Real Worldcited by 104
  22. 2022Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RLcited by 103
  23. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  24. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
  25. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  26. 2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
  27. 2024DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformerscited by 96
  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. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillationcited by 93
  32. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 92
  33. 2023The Waymo Open Sim Agents Challengecited by 86
  34. 2019Urban Driving with Conditional Imitation Learningcited 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. 2021VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehiclescited by 81

50 most cited of 479 All 479 (JSON)