2026-09-18T20:59:24Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25922 2026-09-05T19:32:20Z ddc:004 open_access

HASCO: A Hybrid AI Simulation Compiler for Semantic Accident Reconstruction Jelačić, Edin Gu, Rong Seceleanu, Cristina Xiong, Ning Backeman, Peter Seceleanu, Tiberiu Fei, Zhennan Nouri, Ali Autonomous Driving OpenSCENARIO Large Language Models Scenario Generation Semantic Reconstruction The validation of Automated Driving Systems (ADSs) has shifted from distance-based metrics to Scenario-Based Testing (SBT). Large Language Models (LLMs) have emerged as powerful tools with potential for generating vehicular scenarios at scale. However, generative models, used for direct simulation synthesis, produce inadequate output, therefore necessitating a more structured compilation approach. In this regard, we present HASCO (Hybrid AI Simulation COmpiler), a system that translates natural-language driving scene specifications into executable simulation artifacts (XOSC/XODR files) for the esmini/OpenSCENARIO ecosystem. While LLMs excel at narrative parsing, we demonstrate that direct synthesis of simulation artifacts fails in the vast majority of cases due to hallucinated physics or schema violations. To resolve this, HASCO treats scenario creation as a compilation task rather than a generative one. The pipeline supports three compilation paths: direct synthesis, a Python intermediate (via scenariogeneration), and an ontology-guided path that grounds intent into an intermediate representation (IR) before compilation. We further evaluate a self-judging mechanism for automated repair. Across six operating modes evaluated on 40 real-world accident reports, the ontology-guided compiler and Python-based compiler achieve 95% and 90% executability rates, respectively (compared to 5% for direct synthesis). Additionally, we evaluate outputs on semantic fidelity, positioning HASCO as a robust tool for forensic scene reconstruction. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Edin Jelačić and Rong Gu and Cristina Seceleanu and Ning Xiong and Peter Backeman and Tiberiu Seceleanu and Zhennan Fei and Ali Nouri 2026 Is Part Of OASIcs, Volume 143, 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.AEiC.2026.4 urn:nbn:de:0030-drops-259220 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AEiC.2026.4 eng https://creativecommons.org/licenses/by/4.0/legalcode