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

TL;DR

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, is proposed and an ontology based on specialized domain knowledge of road traffic systems is formalized.

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.

Figures (14)

• Figure 1: Comparison of various methods for infrastructure improvement design. (a) is based on expert knowledge, while (b) represents our proposed approach. Our method not only outputs infrastructure improvement plans for road structures that cause traffic accidents but also generates visual representations of roads after improvement.
• Figure 2: Breakdown of infrastructure improvement process in field of road transportation systems, and overview of how OD-RASE Dataset is constructed on basis of it. Final set of 11 types of road structures causing traffic accidents (top) and 10 types of countermeasures (bottom).
• Figure 3: Schematic diagram of the conventional infrastructure improvement process by expert.
• Figure 4: Proposed ontology-driven dataset construction method. Our method allows for fully automatic generation using VLMs and enhances dataset quality and reliability through filtering with reference graphs. Additionally, such filtering further refines dataset's overall trustworthiness.
• Figure 5: OD-RASE model architecture. Each modality scene images and textual descriptions of traffic risks is encoded, and grounding block captures semantic relationships between image and text.