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Declarative Scenario-based Testing with RoadLogic

arXiv 2603.09455 ↗First posted 10 Mar 2026cs.SE cs.AI cs.LOcited by 0

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Abstract (arXiv)

Scenario-based testing is a key method for cost-effective and safe validation of autonomous vehicles (AVs). Existing approaches rely on imperative scenario definitions, requiring developers to manually enumerate numerous variants to achieve coverage. Declarative languages, such as ASAM OpenSCENARIO DSL (OS2), raise the abstraction level but lack systematic methods for instantiating concrete and specification-compliant scenarios. To our knowledge, currently, no open-source solution provides this capability. We present RoadLogic that bridges declarative OS2 specifications and executable simulations. It uses Answer Set Programming to generate abstract plans satisfying scenario constraints, motion planning to refine the plans into feasible trajectories, and specification-based monitoring to verify correctness. We evaluate RoadLogic on instantiating representative OS2 scenarios executed in the CommonRoad framework. Results show that RoadLogic consistently produces realistic, specification-satisfying simulations within minutes and captures diverse behavioral variants through parameter sampling, thus opening the door to systematic scenario-based testing for autonomous driving systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 10 Mar 2026.

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References · 12

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2017Multi-shot ASP solving with clingocited by 115
  3. 2018Scenic: A Language for Scenario Specification and Scene Generationcited by 79
  4. 2014Clingo = ASP + Control: Preliminary Reportcited by 56
  5. 2018Scenarios for Development, Test and Validation of Automated Vehiclescited by 45
  6. 2022A Survey on Automated Driving System Testing: Landscapes and Trendscited by 36
  7. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  8. 2024FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Drivingcited by 14
  9. 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Reviewcited by 12
  10. 2024Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehiclescited by 3
  11. 2018An Algebraic Framework for Runtime Verificationcited by 2
  12. 2023Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoadcited by 2

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