mlsanityData to 7 Oct 2026

Adaptive Stress Testing for Autonomous Vehicles

arXiv 1902.01909 ↗First posted 5 Feb 2019cs.RO cs.AI cs.LGcited by 70

Not on the map. Why?

Cited by 23 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 10; 2023: 11; 2024: 12; 2025: 8; 2026: 14.

70 papers.

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  1. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  2. 2021Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehiclescited by 27
  3. 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
  4. 2022Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systemscited by 18
  5. 2022How Do We Fail? Stress Testing Perception in Autonomous Vehiclescited by 17
  6. 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Reviewcited by 12
  7. 2021MDPFuzz: Testing Models Solving Markov Decision Processescited by 11
  8. 2024Learning to Drive via Asymmetric Self-Playcited by 11
  9. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policiescited by 10
  10. 2024SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generationcited by 10
  11. 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllerscited by 9
  12. 2021Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systemscited by 8
38 more
  1. 2021Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithmcited by 7
  2. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  3. 2021Assisted Robust Reward Designcited by 5
  4. 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
  5. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledgecited by 4
  6. 2022Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithmscited by 4
  7. 2023Adaptive Failure Search Using Critical States from Domain Expertscited by 4
  8. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  9. 2021ARC: Adversarially Robust Control Policies for Autonomous Vehiclescited by 3
  10. 2021Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systemscited by 3
  11. 2021Querying Labelled Data with Scenario Programs for Sim-to-Real Validationcited by 3
  12. 2022Causal Scene BERT: Improving object detection by searching for challenging groups of datacited by 3
  13. 2023Model-based Validation as Probabilistic Inferencecited by 3
  14. 2023Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Renderingcited by 3
  15. 2024ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Promptscited by 3
  16. 2024Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Reviewcited by 3
  17. 2025Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentionscited by 3
  18. 2026ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Drivingcited by 3
  19. 2021Adaptive Stress Testing for Adversarial Learning in a Financial Environmentcited by 2
  20. 2022ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Drivingcited by 2
  21. 2022A Deep Reinforcement Learning Approach to Rare Event Estimationcited by 2
  22. 2024Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learningcited by 2
  23. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Studycited by 2
  24. 2024Rate-Informed Discovery via Bayesian Adaptive Multifidelity Samplingcited by 2
  25. 2024Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approachcited by 2
  26. 2026Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Developmentcited by 2
  27. 2022Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Samplingcited by 1
  28. 2022An Active Learning Reliability Method for Systems with Partially Defined Performance Functionscited by 1
  29. 2023Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehiclescited by 1
  30. 2023Attacking Motion Planners Using Adversarial Perception Errorscited by 1
  31. 2023STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automationcited by 1
  32. 2024A novel framework for adaptive stress testing of autonomous vehicles in multi-lane roadscited by 1
  33. 2024LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantizationcited by 1
  34. 2025Controllable Latent Diffusion for Traffic Simulationcited by 1
  35. 2025Post-Convergence Sim-to-Real Policy Transfer: A Principled Alternative to Cherry-Pickingcited by 1
  36. 2025RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusioncited by 1
  37. 2025Leveraging Reward Models for Guiding Code Review Comment Generationcited by 1
  38. 2025Diffusion Models for Safety Validation of Autonomous Driving Systemscited by 1

50 most cited of 70 All 70 (JSON)