用逻辑编程自动生成符合规范的自动驾驶测试场景
Declarative Scenario-based Testing with RoadLogic
- 用答案集编程生成满足约束的抽象驾驶计划
- 在分钟级内完成真实可行的仿真，覆盖多种行为变体
- 适合自动驾驶系统验证团队提升测试效率
基于场景的测试是低成本、安全验证自动驾驶车辆的关键方法。现有方法依赖指令式场景定义，需人工枚举大量变体以实现覆盖。声明式语言如ASAM OpenSCENARIO DSL（OS2）虽提升了抽象层级，但缺乏系统化生成具体且符合规范场景的能力。目前尚无开源方案解决此问题。本文提出RoadLogic，将声明式OS2规范与可执行仿真相连接：利用答案集编程生成满足约束的抽象计划，通过运动规划将其细化为可行轨迹，并基于规范监控验证正确性。我们在CommonRoad框架中对代表性OS2场景进行评估，结果表明RoadLogic能在数分钟内持续生成符合规范的真实仿真，通过参数采样捕捉多样行为变体，为自动驾驶系统提供系统化场景测试新路径。
原文摘要 · Abstract (English)
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.
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