An Adaptive Ontology–LLM Framework for Simulation-Based Reenactment of Korean Traffic Accidents

Simulation-based testing is fundamental for validating automated driving systems (ADSs), and real-world traffic accident records provide realistic scenario data. However, converting such records into executable simulations requires addressing structural incompleteness, geographic bias in large language models (LLMs), and code-generation errors. This paper proposes an adaptive ontology–LLM framework that generates Scenic scenarios executable in CARLA from structured accident records obtained from the Korea Road Traffic Authority’s Traffic Accident Analysis System (TAAS). The framework comprises five modules connected through explicit interfaces and feedback loops: Ontology-Based Data Structuring, Missing Attribute Inference, Korean-Context Filtering, Scenic Scenario Code Generation, and Simulation Validator. Only Korean-Context Filtering is country-specific, supporting future adaptation by replacing this module. Across three complete executions of the final pipeline with the map-topology and feasible-placement pre-checks applied, Ontology-Based Data Structuring, Missing Attribute Inference, and Korean-Context Filtering achieved mean success rates of 91.00%, 92.37%, and 94.07%, respectively. The mean Step 3 cumulative success rate was 78.30%, and the mean end-to-end success rate was 61.83% (range: 59.00–63.50%). Korean-Context Filtering achieved an F1 score of 0.9246 on the Original Korean-Context Evaluation Set and a mean F1 score of 0.8708 on the Additional Korean-Context Evaluation Set. The results demonstrate the feasibility of integrating ontology mapping, causal reasoning-based attribute inference, geographic-bias mitigation, and executable scenario generation in a unified framework.