发表机构
College of Automotive and Energy Engineering, Tongji University; James Watt School of Engineering, University of Glasgow（同济大学汽车与能源工程学院; 格拉斯哥大学詹姆斯·瓦特工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对驾驶风险评估中数据稀缺问题，提出基于比较的序数风险学习框架，从成对监督中学习风险分数，通过三种数据来源进行成对比较并实现三种参数化实例，实验表明其在多方面优于基线，可为自动驾驶提供可靠风险评估。
AI 中文摘要
实时驾驶风险评估为主动安全提供重要基础，可在不良后果发生前识别并量化道路交互危险。但因碰撞数据和帧级风险标签稀缺，现有方法常依赖代理目标，可能无法准确反映真实碰撞风险。本文提出基于比较的序数风险学习框架，从驾驶数据的成对监督中学习与碰撞相关的风险分数，直接对相对风险排序建模，无需数值帧级风险标签。通过三种事件结构化驾驶数据来源进行成对比较，实现三种风险评分函数参数化实例。在两个自然驾驶数据集上评估，结果表明该框架在召回率、警告精度和提前时间上优于基于代理的基线，能为自动驾驶系统提供更可靠风险评估，有助于主动安全研究。
英文摘要
Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur. However, due to the scarcity of collision data and frame-level risk labels, existing driving risk assessment methods often rely on surrogate objectives, which may imperfectly align with true collision risk and not faithfully reflect the relative danger of driving interaction. This paper proposes a comparison-based ordinal risk learning framework that learns collision-relevant risk scores from pairwise supervision in driving data, directly modeling relative risk ordering without requiring numerical frame-level risk labels. We derive pairwise comparisons from three sources of event-structured driving data for such ordinal risk learning: temporal progression within safety-critical sequences, event-level contrast between dangerous and normal interactions, and physics-based counterfactual perturbations. On this basis, instantiations with three risk-scoring function parameterizations are implemented, including directly learning risk scores from comparison data, and aligning existing single or multiple surrogate-based risk models. The proposed framework is evaluated on the 100-Car and SHRP2 naturalistic driving datasets using a proactive collision warning task. Results show that the proposed framework improves high-recall risk discrimination, warning precision, and warning lead time over representative surrogate-based baselines across both in-distribution and out-of-distribution evaluations. These results suggest that the proposed framework can contribute to proactive safety research by providing more reliable risk assessment for automated driving systems and safety-critical driving interactions.
Comments15 pages, 5 figures
