Declarative Scenario-based Testing with RoadLogic
AI 摘要
RoadLogic将声明式OS2场景转换为可执行仿真,实现自动驾驶系统测试。
主要贡献
- 提出RoadLogic框架
- 使用Answer Set Programming生成抽象计划
- 实现了基于规范的监控和验证
方法论
利用Answer Set Programming生成计划,通过运动规划优化轨迹,并进行规范验证。
原文摘要
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 OpenSCENARIO DSL (OS2), raise the abstraction level but lack systematic methods for instantiating concrete, specification-compliant scenarios as simulations. 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 as simulations 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.