mlsanityData to 7 Oct 2026

Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

arXiv 1903.00640 ↗First posted 2 Mar 2019cs.ROcited by 48

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

Abstract (arXiv)

The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.

Key facts. Cited by 48 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. Builds on 15 of its 25 references that are in the map. First posted on arXiv 2 Mar 2019.

Builds on

15 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst7 Dec
  2. 2018Conditional Affordance Learning for Driving in Urban Environments18 Jun

Other references · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2016End to End Learning for Self-Driving Carscited by 1,000
  5. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  6. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  7. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
  8. 2017Inverse Reward Designcited by 246
  9. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 147
  10. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  11. 2017Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Carcited by 65
  12. 2018Deep Object-Centric Policies for Autonomous Drivingcited by 41
1 more
  1. 2017A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learningcited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 8; 2022: 8; 2023: 15; 2024: 8; 2025: 5; 2026: 3.

48 papers.

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  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  2. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  3. 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Drivingcited by 24
  4. 2021Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Modelscited by 22
  5. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learningcited by 18
  6. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networkscited by 13
  7. 2024Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networkscited by 12
  8. 2020PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Drivingcited by 11
  9. 2021Explainable Hierarchical Imitation Learning for Robotic Drink Pouringcited by 11
  10. 2024VIEW: Visual Imitation Learning with Waypointscited by 10
  11. 2022Cooperative Behavior Planning for Automated Driving using Graph Neural Networkscited by 8
  12. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Reviewcited by 7
36 more
  1. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Drivingcited by 7
  2. 2024Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrationscited by 7
  3. 2023Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Drivingcited by 6
  4. 2023Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representationscited by 6
  5. 2021Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Drivingcited by 5
  6. 2022Tackling Real-World Autonomous Driving using Deep Reinforcement Learningcited by 5
  7. 2024RAIL: Reachability-Aided Imitation Learning for Safe Policy Executioncited by 5
  8. 2023Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learningcited by 4
  9. 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Drivingcited by 4
  10. 2023IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisorscited by 4
  11. 2023Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Controlcited by 4
  12. 2024Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Drivingcited by 4
  13. 2022Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learningcited by 3
  14. 2023Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networkscited by 3
  15. 2023NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perceptioncited by 3
  16. 2025Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planningcited by 3
  17. 2021Learning robust driving policies without online explorationcited by 2
  18. 2022Fail-Safe Adversarial Generative Imitation Learningcited by 2
  19. 2022Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path constructioncited by 2
  20. 2021Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualitiescited by 1
  21. 2022MPC-based Imitation Learning for Safe and Human-like Autonomous Drivingcited by 1
  22. 2023Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehiclescited by 1
  23. 2024Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environmentcited by 1
  24. 2025ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuningcited by 1
  25. 2025An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learningcited by 1
  26. 2025Follow-Me in Micro-Mobility with End-to-End Imitation Learningcited by 1
  27. 2026AppleVLM: End-to-end Autonomous Driving with Advanced Perception and Planning-Enhanced Vision-Language Modelscited by 1
  28. 2022Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehiclescited by 0
  29. 2023Specification-Guided Data Aggregation for Semantically Aware Imitation Learningcited by 0
  30. 2023SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Drivingcited by 0
  31. 2023Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environmentscited by 0
  32. 2024Collision Probability Distribution Estimation via Temporal Difference Learningcited by 0
  33. 20243CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Drivingcited by 0
  34. 2025Energy-Efficient Autonomous Driving with Adaptive Perception and Robust Decisioncited by 0
  35. 2026Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kineticscited by 0
  36. 2026End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  2. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
  3. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
  4. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving
  5. 2019Urban Driving with Conditional Imitation Learning
  6. 2022Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
  7. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving
  8. 2023Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies