mlsanityData to 7 Oct 2026

Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation

arXiv 1908.01046 ↗First posted 2 Aug 2019cs.RO cs.AI cs.LGcited by 33

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Abstract (arXiv)

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have been proposed to aid in validation. AST formulates the problem of finding the most likely failure scenarios as a Markov decision process, which can be solved using reinforcement learning. In practice, AST tends to find scenarios where failure is unavoidable and tends to repeatedly discover the same types of failures of a system. This work addresses these issues by encoding domain relevant information into the search procedure. With this modification, the AST method discovers a larger and more expressive subset of the failure space when compared to the original AST formulation. We show that our approach is able to identify useful failure scenarios of an autonomous vehicle policy.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2019.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 9; 2024: 6; 2025: 5; 2026: 3.

33 papers.

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  1. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  2. 2021Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehiclescited by 21
  3. 2022Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systemscited by 18
  4. 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Reviewcited by 12
  5. 2024Learning to Drive via Asymmetric Self-Playcited by 11
  6. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policiescited by 10
  7. 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllerscited by 9
  8. 2021Safety of the Intended Driving Behavior Using Rulebookscited by 8
  9. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  10. 2022Risk-Driven Design of Perception Systemscited by 4
  11. 2023Adaptive Failure Search Using Critical States from Domain Expertscited by 4
  12. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
21 more
  1. 2022Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verificationcited by 3
  2. 2023Model-based Validation as Probabilistic Inferencecited by 3
  3. 2023DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Datacited by 3
  4. 2022A Deep Reinforcement Learning Approach to Rare Event Estimationcited by 2
  5. 2024Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learningcited by 2
  6. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Studycited by 2
  7. 2025AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Modelscited by 2
  8. 2022Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulatorcited by 1
  9. 2023Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehiclescited by 1
  10. 2023Attacking Motion Planners Using Adversarial Perception Errorscited by 1
  11. 2024A novel framework for adaptive stress testing of autonomous vehicles in multi-lane roadscited by 1
  12. 2024Generating Critical Scenarios for Testing Automated Driving Systemscited by 1
  13. 2025Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testingcited by 1
  14. 2023Conveying Autonomous Robot Capabilities through Contrasting Behaviour Summariescited by 0
  15. 2023ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Eventscited by 0
  16. 2025Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictionscited by 0
  17. 2025Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarioscited by 0
  18. 2025Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptancecited by 0
  19. 2026Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehiclescited by 0
  20. 2026Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Searchcited by 0
  21. 2026Coverage Aware Active Evaluation for Failure Discovery with Paired Systemscited by 0