# OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving Source: [OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving]() Collection index: [llms.txt]() Data updated: 2026-03-10T05:00:05.424Z Authors: Kota Shimomura, Masaki Nambata, Atsuya Ishikawa, Ryota Mimura, Takayuki Kawabuchi, Takayoshi Yamashita, Koki Inoue Published: 2026-03-06T06:08:40.000Z ## Abstract Although autonomous driving systems demonstrate high perception performance, they still face limitations when handling rare situations or complex road structures. Such road infrastructures are designed for human drivers, safety improvements are typically introduced only after accidents occur. This reactive approach poses a significant challenge for autonomous systems, which require proactive risk mitigation. To address this issue, we propose OD-RASE, a framework for enhancing the safety of autonomous driving systems by detecting road structures that cause traffic accidents and connecting these findings to infrastructure development. First, we formalize an ontology based on specialized domain knowledge of road traffic systems. In parallel, we generate infrastructure improvement proposals using a large-scale visual language model (LVLM) and use ontology-driven data filtering to enhance their reliability. This process automatically annotates improvement proposals on pre-accident road images, leading to the construction of a new dataset. Furthermore, we introduce the Baseline approach (OD-RASE model), which leverages LVLM and a diffusion model to produce both infrastructure improvement proposals and generated images of the improved road environment. Our experiments demonstrate that ontology-driven data filtering enables highly accurate prediction of accident-causing road structures and the corresponding improvement plans. We believe that this work contributes to the overall safety of traffic environments and marks an important step toward the broader adoption of autonomous driving systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Infrastructure improvement proposal on BDD100K (val)](): infrastructure improvement proposal; BDD100K (val); Recall: 70.22. - [Infrastructure improvement proposal on BDD100K (test)](): infrastructure improvement proposal; BDD100K (test); Recall: 69.17. - [Infrastructure Improvement Proposal on Mapillary (val)](): infrastructure improvement proposal; Mapillary (val); Recall: 76.84. - [Infrastructure Improvement Proposal on Mapillary (test)](): infrastructure improvement proposal; Mapillary (test); Recall: 77.77. - [Accident-causing road structure prediction on BDD100K (val)](): Accident-causing road structure prediction; BDD100K (val); Recall: 83.14. - [Accident-causing Road Structure Prediction on BDD100K (test)](): Accident-causing road structure prediction; BDD100K (test); Recall: 83.72. ## Original sources and implementation [Original paper]() [PDF]()