{"paper":{"title":"Compiling OpenSCENARIO 2.1 for Scenario-Based Testing in CARLA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A compiler pipeline converts OpenSCENARIO 2.1 scenarios into executable CARLA behavior trees by mapping the standard ontology through a custom registry.","cross_cats":["cs.PL","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Lasanthi Gamage, Thoshitha Gamage","submitted_at":"2026-04-07T20:30:06Z","abstract_excerpt":"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 direct"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a custom method registry can map the full OpenSCENARIO 2.1 ontology to CARLA's procedural API without external logic solvers or loss of expressiveness for concurrent and dynamic behaviors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A multi-pass compiler using ANTLR4 and py_trees translates OpenSCENARIO 2.1 DSL into CARLA behaviors, demonstrated on a multi-actor cut-in scenario.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A compiler pipeline converts OpenSCENARIO 2.1 scenarios into executable CARLA behavior trees by mapping the standard ontology through a custom registry.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"adba2a324e7184957f5d83fa54bb7e7822132ce237e369a121b9f8e1d4dcb648"},"source":{"id":"2604.16452","kind":"arxiv","version":2},"verdict":{"id":"f9abdca5-8764-4274-99fd-2feb78d045b7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:23:06.376561Z","strongest_claim":"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.","one_line_summary":"A multi-pass compiler using ANTLR4 and py_trees translates OpenSCENARIO 2.1 DSL into CARLA behaviors, demonstrated on a multi-actor cut-in scenario.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a custom method registry can map the full OpenSCENARIO 2.1 ontology to CARLA's procedural API without external logic solvers or loss of expressiveness for concurrent and dynamic behaviors.","pith_extraction_headline":"A compiler pipeline converts OpenSCENARIO 2.1 scenarios into executable CARLA behavior trees by mapping the standard ontology through a custom registry."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.16452/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":17,"sample":[{"doi":"","year":2020,"title":"ASAM OpenSCENARIO XML","work_id":"c100c5fd-ecbb-4508-a633-da77ba7bc25a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"ASAM OpenSCENARIO DSL 2.1.0","work_id":"c66d7e7d-960b-49bc-9273-172581ea20fa","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Declarative Scenario-based Testing with RoadLogic","work_id":"18698f69-78bf-401d-8a1f-5a3660c1e5f5","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"The Y ASE Framework: Holistic Scenario Modeling with Behav- ior Trees","work_id":"8a8da4d1-9e37-4752-8b24-9453a212a0b2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Text2Scenario: Text-driven scenario generation for autonomous driving test","work_id":"59c43b53-5c04-4268-a1a2-dfed7316ca61","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":17,"snapshot_sha256":"31576e23b397165d3e59370022e41f05d2c2b788f4e0615180d1eb99851c0ffa","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}