🤖 AI Summary
Existing autonomous driving simulation frameworks lack native support for OpenSCENARIO 2.x, leading to spatiotemporal drift, event latency, and motion discontinuities during scenario execution. This work proposes the first CARLA-based simulation orchestration framework with native OpenSCENARIO 2.x support. It employs a multi-pass translator to compile the domain-specific language into a type-safe abstract syntax tree and dynamically generates deterministic behavior trees that invoke CARLA’s atomic APIs. This approach achieves, for the first time, an exact mapping from OpenSCENARIO 2.x to CARLA, elevating scenario testing from approximate interpretation to a mathematically rigorous and deterministic execution paradigm. The framework ensures frame-accurate determinism, precise spatial trigger evaluation, and 100-millisecond cross-agent blackboard synchronization under high-concurrency adversarial conditions, strictly adhering to continuous environmental boundary constraints.
📝 Abstract
Scenario-Based Testing predominantly relies on the legacy ASAM OpenSCENARIO 1.x XML standard because existing continuous simulation frameworks lack native execution support for the recently matured v2.x Domain-Specific Language (DSL). Adapting legacy interpreters to evaluate v2.x logic introduces spatiotemporal drift, asynchronous event latencies, and artificial kinematic snapping. Addressing this execution gap, OSC2Runner introduces the first orchestration framework capable of natively mapping the OpenSCENARIO v2.x DSL to CARLA. The framework achieves this by formalizing scenario translation as a compilation pipeline through a multi-pass transpiler architecture. Bypassing static trajectory playback, the architecture synthesizes type-safe Abstract Syntax Trees directly into dynamic deterministic behavior trees (py_trees) natively mapped to CARLA's atomic APIs. Empirical validation in highly concurrent adversarial case studies demonstrates tick-by-tick determinism, exact spatial trigger evaluation, and 100.0 ms cross-actor blackboard synchronization. Kinematic analysis proves the strict adherence to continuous environmental boundaries. This architecture transitions Scenario-Based Testing from approximate behavioral interpretation to mathematically rigorous execution, establishing the deterministic backend required for co-simulation, hardware-in-the-loop testing, and automated LLM-driven generation pipelines.