Declarative Scenario-based Testing with RoadLogic
The open-source tool generates realistic, compliant driving scenarios from declarative specs within minutes.
A team of researchers has introduced RoadLogic, a novel open-source framework designed to automate a critical bottleneck in autonomous vehicle (AV) validation. Currently, testing AVs relies heavily on scenario-based methods, but existing approaches using declarative languages like the OpenSCENARIO DSL (OS2) lack a systematic way to turn high-level specifications into concrete, executable simulations. RoadLogic bridges this gap by employing Answer Set Programming (ASP) to generate abstract plans that satisfy complex scenario constraints, then refines these plans into feasible vehicle trajectories using motion planning. This automated pipeline can generate realistic, compliant simulations within minutes, a task that previously required manual, time-consuming enumeration of variants by developers.
RoadLogic was evaluated by instantiating representative OS2 scenarios within the CommonRoad simulation framework. The results demonstrate its ability to consistently produce specification-satisfying simulations and, through parameter sampling, capture a wide range of behavioral variants crucial for comprehensive testing. This capability "opens the door to systematic scenario-based testing" by providing the first open-source solution that connects declarative specs directly to simulations. The work, accepted at the 29th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2026), addresses a key need for cost-effective and rigorous safety validation as autonomous driving systems advance.
- Automates simulation generation from high-level OpenSCENARIO DSL (OS2) specs using Answer Set Programming and motion planning.
- Produces realistic, specification-compliant driving scenarios within minutes, capturing diverse behavioral variants through parameter sampling.
- First open-source solution to bridge declarative specifications and executable simulations for systematic AV testing.
Why It Matters
It automates a manual, costly process in AV safety validation, enabling more rigorous and scalable testing of self-driving systems.