mlsanityData to 7 Oct 2026

Learning to Drive in a Day

arXiv 1807.00412 ↗First posted 1 Jul 2018cs.LG cs.AI cs.ROcited by 266

Not on the map. Why?

Cited by 136 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control. We use a continuous, model-free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle. This demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision. We discuss the challenges and opportunities to scale this approach to a broader range of autonomous driving tasks.

Key facts. Cited by 266 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 2; 2020: 1; 2021: 62; 2022: 40; 2023: 37; 2024: 41; 2025: 60; 2026: 23.

266 papers.

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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. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  4. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  6. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 343
  7. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  8. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
  9. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  10. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  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. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  3. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  4. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158
  5. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  6. 2025Diffusion-Based Planning for Autonomous Driving with Flexible Guidancecited by 138
  7. 2022Learning from All Vehiclescited by 135
  8. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
  9. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  10. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
  11. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  12. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  13. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  14. 2021Reward (Mis)design for Autonomous Drivingcited by 102
  15. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  16. 2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
  17. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillationcited by 93
  18. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  19. 2021Benchmarks for Deep Off-Policy Evaluationcited by 89
  20. 2019Urban Driving with Conditional Imitation Learningcited by 84
  21. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  22. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  23. 2021RobustART: Benchmarking Robustness on Architecture Design and Training Techniquescited by 78
  24. 2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
  25. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Drivingcited by 71
  26. 2021Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximationcited by 69
  27. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policiescited by 68
  28. 2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
  29. 2022Challenges and Opportunities in Offline Reinforcement Learning from Visual Observationscited by 63
  30. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  31. 2023Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Modelscited by 59
  32. 2021Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworkscited by 56
  33. 2024SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Drivingcited by 46
  34. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
  35. 2024End-to-End Autonomous Driving through V2X Cooperationcited by 42
  36. 2025How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspectivecited by 41
  37. 2022Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Riskcited by 40
  38. 2025DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Drivingcited by 39

50 most cited of 266 All 266 (JSON)