将OpenSCENARIO 2.1直接编译为CARLA可执行行为，实现可复现的大规模场景测试。
Compiling OpenSCENARIO 2.1 for Scenario-Based Testing in CARLA
- 多阶段编译架构，从DSL直接生成CARLA行为树。
- 支持并发动作、动态表达式与异步信号，验证通过多车切入避让场景。
- 免去外部求解器，适配自动驾驶测试人员与仿真开发者。
尽管ASAM OpenSCENARIO 2.1领域特定语言（DSL）支持意图驱动的声明式场景编写，但其在CARLA等开源仿真器中的集成仍受限于旧版解析器。本文提出一种多阶段现代编译架构，可将OpenSCENARIO 2.1 DSL直接转换为可执行的CARLA行为。该流程包含基于ANTLR4的前端生成抽象语法树（AST）、语义中间层与运行时后端，后者合成确定性py_trees行为树。通过自定义方法注册表，将标准化领域本体直接映射至CARLA的程序化API，避免使用外部逻辑求解器。选取一组经验证的场景作为演示，包括多智能体切入与避让操作，验证了编译器处理并发动作、动态数学表达式及异步信号的能力。该框架建立了可复现、大规模场景测试的功能基线，并为未来采用C++优化以降低当前基于Python的计算开销铺平道路。
原文摘要 · Abstract (English)
While the ASAM OpenSCENARIO 2.1 Domain-Specific Language (DSL) enables declarative, intent-driven authoring for Scenario-Based Testing (SBT), its integration into open-source simulators like CARLA remains limited by legacy parsers. We propose a multi-pass modern compiler architecture that translates the OpenSCENARIO 2.1 DSL directly into executable CARLA behaviors. The pipeline features an ANTLR4 frontend for Abstract Syntax Tree (AST) generation, a semantic middle-end, and a runtime backend that synthesizes deterministic py_trees behavior trees. Mapping the standardized domain ontology directly to CARLA's procedural API via a custom method registry eliminates the need for external logic solvers. A demonstrative multi-actor cut-in and evasive maneuver, selected from a wider suite of validated scenarios, confirms the compiler's ability to process concurrent actions, dynamic mathematical expressions, and asynchronous signaling. This framework establishes a functional baseline for reproducible, large-scale SBT, paving the way for future C++ optimizations to mitigate current Python-based computational overhead.
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