mlsanityData to 7 Oct 2026

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

arXiv 2001.08726 ↗First posted 23 Jan 2020cs.RO cs.CV cs.LGcited by 99

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Cited by 59 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this paper, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with a certain intermediate property in today's modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests with a simulated autonomous car in CARLA show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned policy is able to provide a better explanation of how the car reasons about the driving environment. The codes and videos of this work are available at our github repo and project website.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2020.

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Citing papers per year: 2021: 20; 2022: 11; 2023: 19; 2024: 20; 2025: 21; 2026: 8.

99 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  3. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  4. 2023DREAMWALKER: Mental Planning for Continuous Vision-Language Navigationcited by 82
  5. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  6. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  7. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  8. 2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
  9. 2024DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Drivingcited by 67
  10. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  11. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  12. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 46
38 more
  1. 2022Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Modelcited by 44
  2. 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priorscited by 36
  3. 2023MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representationscited by 33
  4. 2022A Survey of Explainable Reinforcement Learningcited by 31
  5. 2021Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safetycited by 30
  6. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancementcited by 28
  7. 2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 25
  8. 2022TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Drivingcited by 23
  9. 2022End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agentcited by 21
  10. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learningcited by 18
  11. 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Drivingcited by 17
  12. 2023Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Drivingcited by 16
  13. 2021Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlookscited by 15
  14. 2023Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?cited by 13
  15. 2022Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptationcited by 12
  16. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  17. 2025Dream to Drive with Predictive Individual World Modelcited by 11
  18. 2024Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrationscited by 7
  19. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  20. 2024M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Drivingcited by 7
  21. 2024Joint Perception and Prediction for Autonomous Driving: A Surveycited by 7
  22. 2023Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representationscited by 6
  23. 2024Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?cited by 6
  24. 2025RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policiescited by 6
  25. 2021Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learningcited by 5
  26. 2021A Versatile and Efficient Reinforcement Learning Framework for Autonomous Drivingcited by 5
  27. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  28. 2024Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learningcited by 5
  29. 2025Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planningcited by 5
  30. 2021A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarioscited by 4
  31. 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representationscited by 4
  32. 2021Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learningcited by 4
  33. 2025ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systemscited by 4
  34. 2025Interpretable Decision-Making for End-to-End Autonomous Drivingcited by 4
  35. 2021Steadily Learn to Drive with Virtual Memorycited by 3
  36. 2021Goal-constrained Sparse Reinforcement Learning for End-to-End Drivingcited by 3
  37. 2023Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environmentscited by 3
  38. 2023Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learningcited by 3

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