mlsanityData to 7 Oct 2026

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

arXiv 2006.14911 ↗First posted 26 Jun 2020cs.LG cs.RO stat.MLcited by 139

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Cited by 30 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaptation to OOD scenes can mitigate their adverse effects. In this paper, we highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called robust imitative planning (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. If the model's uncertainty is too great to suggest a safe course of action, the model can instead query the expert driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term adaptive robust imitative planning (AdaRIP). Our methods outperform current state-of-the-art approaches in the nuScenes prediction challenge, but since no benchmark evaluating OOD detection and adaption currently exists to assess control, we introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 2; 2021: 27; 2022: 32; 2023: 24; 2024: 25; 2025: 19; 2026: 10.

139 papers.

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  1. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  2. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  4. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  5. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  6. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  7. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  8. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  9. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  10. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Drivingcited by 141
  11. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
  12. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
38 more
  1. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  2. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  3. 2024Real-Time Anomaly Detection and Reactive Planning with Large Language Modelscited by 67
  4. 2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulationcited by 59
  5. 2024Driving Everywhere with Large Language Model Policy Adaptationcited by 53
  6. 2022Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Taskscited by 49
  7. 2022Nonparametric Uncertainty Quantification for Single Deterministic Neural Networkcited by 36
  8. 2022A System-Level View on Out-of-Distribution Data in Roboticscited by 33
  9. 2022Block Selection Method for Using Feature Norm in Out-of-distribution Detectioncited by 30
  10. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  11. 2021PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learningcited by 23
  12. 2021Learn-to-Race: A Multimodal Control Environment for Autonomous Racingcited by 23
  13. 2021Causal Navigation by Continuous-time Neural Networkscited by 23
  14. 2021CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoningcited by 23
  15. 2021Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Modelscited by 22
  16. 2021Learning by Watchingcited by 22
  17. 2022Shifts 2.0: Extending The Dataset of Real Distributional Shiftscited by 22
  18. 2022Interpretable Self-Aware Neural Networks for Robust Trajectory Predictioncited by 22
  19. 2024VLM-MPC: Vision Language Foundation Model (VLM)-Guided Model Predictive Controller (MPC) for Autonomous Drivingcited by 22
  20. 2022End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agentcited by 21
  21. 2023GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradientscited by 21
  22. 2023Segment Every Out-of-Distribution Objectcited by 21
  23. 2022Improving Policy Optimization with Generalist-Specialist Learningcited by 19
  24. 2023Learning to Extrapolate: A Transductive Approachcited by 19
  25. 2021Dealing with the Unknown: Pessimistic Offline Reinforcement Learningcited by 18
  26. 2021Causal-based Time Series Domain Generalization for Vehicle Intention Predictioncited by 17
  27. 2024Partially Observable Task and Motion Planning with Uncertainty and Risk Awarenesscited by 17
  28. 2024Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimationcited by 17
  29. 2024MetaOOD: Automatic Selection of OOD Detection Modelscited by 16
  30. 2023Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detectioncited by 14
  31. 2024Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detectioncited by 14
  32. 2025Handling Out-of-Distribution Data: A Surveycited by 14
  33. 2022RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detectioncited by 13
  34. 2023AUTO: Adaptive Outlier Optimization for Test-Time OOD Detectioncited by 12
  35. 2020PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Drivingcited by 11
  36. 2021Disentangling Semantic-to-visual Confusion for Zero-shot Learningcited by 11
  37. 2022Distributionally Adaptive Meta Reinforcement Learningcited by 11
  38. 2024SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generationcited by 10

50 most cited of 139 All 139 (JSON)