mlsanityData to 7 Oct 2026

A Survey of End-to-End Driving: Architectures and Training Methods

arXiv 2003.06404 ↗First posted 13 Mar 2020cs.AI cs.ROcited by 123

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Cited by 78 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 21; 2022: 7; 2023: 31; 2024: 23; 2025: 25; 2026: 15.

123 papers.

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  1. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 527
  2. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  3. 2023Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Drivingcited by 309
  4. 2023Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?cited by 242
  5. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  6. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  7. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 158
  8. 2025End-to-End Driving with Online Trajectory Evaluation via BEV World Modelcited by 145
  9. 2025Diffusion-Based Planning for Autonomous Driving with Flexible Guidancecited by 138
  10. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  11. 2023Dolphins: Multimodal Language Model for Drivingcited by 124
  12. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
38 more
  1. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  2. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  3. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  4. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  5. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Drivingcited by 71
  6. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 65
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  8. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  9. 2023All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenescited by 40
  10. 2025CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Drivingcited by 31
  11. 2021VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigationcited by 27
  12. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  13. 2025Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Drivingcited by 24
  14. 2022SoK: On the Semantic AI Security in Autonomous Drivingcited by 22
  15. 2025Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modelingcited by 22
  16. 2022End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agentcited by 21
  17. 2021Adversarial Driving: Attacking End-to-End Autonomous Drivingcited by 19
  18. 2021What data do we need for training an AV motion planner?cited by 15
  19. 2024FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Drivingcited by 14
  20. 2024Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMscited by 13
  21. 2025TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learningcited by 13
  22. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
  23. 2020Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learningcited by 11
  24. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learningcited by 11
  25. 2024On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Drivingcited by 10
  26. 2021Behavioral Research and Practical Models of Drivers' Attentioncited by 9
  27. 2021Self-Supervised Action-Space Prediction for Automated Drivingcited by 9
  28. 2025DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modelingcited by 9
  29. 2025DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policycited by 9
  30. 2021SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Controlcited by 8
  31. 2021Self-supervised Point Cloud Representation Learning via Separating Mixed Shapescited by 8
  32. 2023Scaling Vision-based End-to-End Driving with Multi-View Attention Learningcited by 8
  33. 2022LiDAR-as-Camera for End-to-End Drivingcited by 7
  34. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Reviewcited by 7
  35. 2024NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learningcited by 7
  36. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  37. 2021Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Imagescited by 6
  38. 2024Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencodercited by 6

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