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Fundamental Considerations around Scenario-Based Testing for Automated Driving

arXiv 2005.04045 ↗First posted 8 May 2020cs.SEcited by 15

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Abstract (arXiv)

The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification and validation activities are guided by a combination of ISO 26262 and ISO/PAS 21448, together with distance-based testing. Starting at SAE Level 3, such approaches become infeasible, resulting in the need for novel methods. Scenario-based testing is regarded as a possible enabler for verification and validation of automated vehicles. Its effectiveness, however, rests on the consistency and substantiality of the arguments used in each step of the process. In this work, we sketch a generic framework around scenario-based testing and analyze contemporary approaches to the individual steps. For each step, we describe its function, discuss proposed approaches and solutions, and identify the underlying arguments, principles and assumptions. As a result, we present a list of fundamental considerations for which evidences need to be gathered in order for scenario-based testing to support the homologation of automated vehicles.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2020.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 2; 2024: 1; 2025: 0; 2026: 6.

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  1. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  2. 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Reviewcited by 12
  3. 2021A Review of Testing Object-Based Environment Perception for Safe Automated Drivingcited by 9
  4. 2023A Scenario-Based Functional Testing Approach to Improving DNN Performancecited by 7
  5. 2021Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesiscited by 3
  6. 2022Grasping Causality for the Explanation of Criticality for Automated Drivingcited by 2
  7. 2022Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulatorcited by 1
  8. 2023Graph Convolutional Networks for Complex Traffic Scenario Classificationcited by 0
  9. 2024Causality-based Transfer of Driving Scenarios to Unseen Intersectionscited by 0
  10. 2026Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAEcited by 0
  11. 2026From Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testingcited by 0
  12. 2026ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Drivingcited by 0
3 more
  1. 2026AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustnesscited by 0
  2. 2026CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generationcited by 0
  3. 2026Towards Illusions Awareness in Cyber-Physical System's Designcited by 0