mlsanityData to 14 Oct 2026

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

arXiv 2110.08664 ↗First posted 16 Oct 2021cs.SE cs.AI cs.SYcited by 12

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Abstract (arXiv)

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 31 of its 178 references that are in the map. First posted on arXiv 16 Oct 2021.

Builds on

31 of 178 references.

References · 31

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19 more
  1. 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 43
  2. 2019Towards Corner Case Detection for Autonomous Drivingcited by 39
  3. 2019Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validationcited by 33
  4. 2017Compositional Falsification of Cyber-Physical Systems with Machine Learning Componentscited by 32
  5. 2018Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Componentscited by 29
  6. 2016Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Explorationcited by 26
  7. 2019Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodologycited by 25
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  9. 2019Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Componentscited by 18
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  11. 2020Fundamental Considerations around Scenario-Based Testing for Automated Drivingcited by 15
  12. 2019Efficient Autonomy Validation in Simulation with Adaptive Stress Testingcited by 13
  13. 2019Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studiescited by 10
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 3; 2025: 0; 2026: 1.

12 papers.

  1. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  2. 2024Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generationcited by 19
  3. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  4. 2022Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Drivingcited by 7
  5. 2024A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lightingcited by 3
  6. 2022ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Drivingcited by 2
  7. 2022Learning Critical Scenarios in Feedback Control Systems for Automated Drivingcited by 1
  8. 2023Attacking Motion Planners Using Adversarial Perception Errorscited by 1
  9. 2024Generating Critical Scenarios for Testing Automated Driving Systemscited by 1
  10. 2022A Real-time Critical-scenario-generation Framework for Testing Autonomous Driving Systemcited by 0
  11. 2023Vectorized Scenario Description and Motion Prediction for Scenario-Based Testingcited by 0
  12. 2026Declarative Scenario-based Testing with RoadLogiccited by 0