mlsanityData to 7 Oct 2026

Conditional Affordance Learning for Driving in Urban Environments

arXiv 1806.06498 ↗First posted 18 Jun 2018cs.RO cs.LG cs.SYcited by 70

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 18 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 1; 2020: 2; 2021: 23; 2022: 13; 2023: 16; 2024: 8; 2025: 4; 2026: 3.

70 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  3. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 343
  4. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  5. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  6. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  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. 2023A Language Agent for Autonomous Drivingcited by 161
  10. 2022Learning from All Vehiclescited by 135
  11. 2021Learning to drive from a world on railscited by 114
  12. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
38 more
  1. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  2. 2022COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehiclescited by 95
  3. 2023Hidden Biases of End-to-End Driving Modelscited by 95
  4. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  5. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  6. 2024Embodied Understanding of Driving Scenarioscited by 63
  7. 2021Multi-task Learning with Attention for End-to-end Autonomous Drivingcited by 49
  8. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safetycited by 48
  9. 2025iPad: Iterative Proposal-centric End-to-End Autonomous Drivingcited by 41
  10. 2024Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?cited by 38
  11. 2023Rethinking Closed-loop Training for Autonomous Drivingcited by 29
  12. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  13. 2022OCTET: Object-aware Counterfactual Explanationscited by 28
  14. 2021Causal Navigation by Continuous-time Neural Networkscited by 23
  15. 2021Learning by Watchingcited by 22
  16. 2025Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advancescited by 21
  17. 2021Deep Structured Reactive Planningcited by 18
  18. 2021Learning Interactive Driving Policies via Data-driven Simulationcited by 17
  19. 2023Coaching a Teachable Studentcited by 17
  20. 2022CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Drivingcited by 16
  21. 2022Object Detection in Autonomous Vehicles: Status and Open Challengescited by 14
  22. 2022Learning Robotic Navigation from Experience: Principles, Methods, and Recent Resultscited by 13
  23. 2024Instruct Large Language Models to Drive like Humanscited by 11
  24. 2021DROID: Driver-centric Risk Object Identificationcited by 9
  25. 2020Driving Through Ghosts: Behavioral Cloning with False Positivescited by 8
  26. 2020Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectorycited by 8
  27. 2024CarFormer: Self-Driving with Learned Object-Centric Representationscited by 8
  28. 2022LiDAR-as-Camera for End-to-End Drivingcited by 7
  29. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  30. 2024M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Drivingcited by 7
  31. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulatorcited by 7
  32. 2021Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentscited by 6
  33. 2021USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problemscited by 5
  34. 2021A Versatile and Efficient Reinforcement Learning Framework for Autonomous Drivingcited by 5
  35. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledgecited by 4
  36. 2022Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersectionscited by 4
  37. 2022Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Drivingcited by 3
  38. 2023Addressing Limitations of State-Aware Imitation Learning for Autonomous Drivingcited by 3

50 most cited of 70 All 70 (JSON)

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  1. 2019Urban Driving with Conditional Imitation Learning
  2. 2022Dynamic Conditional Imitation Learning for Autonomous Driving
  3. 2019Conditional Vehicle Trajectories Prediction in CARLA Urban Environment
  4. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
  5. 2021Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
  6. 2022Model-Based Imitation Learning for Urban Driving
  7. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  8. 2024Guiding Attention in End-to-End Driving Models