OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving

2026-03-06

AI Summary

This study addresses the limitations of existing road infrastructure, which is predominantly designed for human drivers and often fails to ensure safety in rare or complex scenarios encountered by autonomous vehicles, with safety enhancements typically lagging behind accident occurrences. To bridge this gap, the authors propose OD-RASE, a novel framework that integrates domain-specific traffic ontologies with large vision-language models (LVLMs) to accurately identify road structures contributing to accidents and automatically generate interpretable infrastructure improvement recommendations. The approach combines ontology-driven data filtering, LVLM-based reasoning, and diffusion-model-enhanced visualization, and introduces the first annotated dataset for this task. Experimental results demonstrate that OD-RASE achieves high precision in predicting high-risk road configurations and produces actionable retrofitting strategies, significantly enhancing the proactive safety of autonomous driving systems.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• autonomous driving
• road infrastructure
• risk assessment
• proactive safety
• accident prevention

Innovation

• Ontology-driven
• Risk Assessment
• Visual Language Model
• Autonomous Driving Safety
• Infrastructure Enhancement