Road crashes remain a leading cause of preventable fatalities. Existing prediction models predominantly produce binary outcomes, which offer limited actionable insights for real-time driver feedback. These approaches often lack continuous risk quantification, interpretability, and explicit consideration of vulnerable road users (VRUs), such as pedestrians and cyclists. This research introduces SafeDriver-IQ, a framework that transforms binary crash classifiers into continuous 0-100 safety scores by combining national crash statistics with naturalistic driving data from autonomous vehicles. The framework fuses National Highway Traffic Safety Administration (NHTSA) crash records with Waymo Open Motion Dataset scenarios, engineers domain-informed features, and incorporates a calibration layer grounded in transportation safety literature. Evaluation across 15 complementary analyses indicates that the framework reliably differentiates high-risk from low-risk driving conditions with strong discriminative performance. Findings further reveal that 87% of crashes involve multiple co-occurring risk factors, with non-linear compounding effects that increase the risk to 4.5x baseline. SafeDriver-IQ delivers proactive, explainable safety intelligence relevant to advanced driver-assistance systems (ADAS), fleet management, and urban infrastructure planning. This framework shifts the focus from reactive crash counting to real-time risk prevention.

翻译：道路碰撞事故仍是可预防性死亡的主要原因。现有预测模型主要产生二元结果，对实时驾驶员反馈提供的可操作见解有限。这些方法通常缺乏连续风险量化、可解释性以及对行人、骑行者等弱势道路使用者的明确考量。本研究提出SafeDriver-IQ框架，通过将全国碰撞统计数据与自动驾驶汽车自然驾驶数据相结合，将二元碰撞分类器转化为0-100连续安全评分。该框架融合了美国国家公路交通安全管理局的碰撞记录与Waymo开放运动数据集场景，构建了领域知识驱动的特征，并基于交通安全文献设计了校准层。通过15项互补分析评估表明，该框架能可靠区分高风险与低风险驾驶条件，具有强大的判别性能。研究进一步发现，87%的碰撞事故涉及多个共现风险因素，其非线性复合效应使风险增加至基线水平的4.5倍。SafeDriver-IQ为高级驾驶辅助系统、车队管理和城市基础设施规划提供了主动可解释的安全智能。该框架将关注点从被动的事故统计转向实时风险预防。