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.
Abstract (translated)
尽管自动驾驶系统展示了很高的感知性能，但在处理罕见情况或复杂道路结构时仍然面临限制。这类道路基础设施是为人类驾驶员设计的，安全改进通常只有在事故发生后才被引入。这种被动应对方式对需要主动进行风险缓解的自动驾驶系统构成了重大挑战。为此，我们提出了一种名为OD-RASE的框架，旨在通过检测导致交通事故的道路结构并将其与基础设施发展联系起来来提高自动驾驶系统的安全性。 首先，我们基于道路交通系统的专门领域知识建立了形式化的本体论（ontology）。同时，我们利用大规模视觉语言模型（LVLM）生成基础设施改进提案，并采用本体驱动的数据过滤方法以提升其可靠性。这一过程能够自动对事故前的道路图像进行改进提案的标注，从而构建出一个新的数据集。 此外，我们还引入了基线方法（OD-RASE模型），该方法结合了LVLM和扩散模型来生成基础设施改进提案以及改进后的道路环境图像。 我们的实验表明，基于本体论的数据过滤能够实现对导致事故的道路结构及其相应改进计划的高精度预测。我们认为这项工作有助于改善交通环境的整体安全性，并标志着向更广泛地采用自动驾驶系统迈进的重要一步。
URL
https://arxiv.org/abs/2603.05936
