Accurately and proactively alerting drivers or automated systems to emerging collisions is crucial for road safety, particularly in highly interactive and complex urban environments. Existing methods either require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are tailored to limited scenarios. Here we present the Generalised Surrogate Safety Measure (GSSM), a data-driven approach that learns collision risk from naturalistic driving without the need for crash or risk labels. Trained over multiple datasets and evaluated on 2,591 real-world crashes and near-crashes, a basic GSSM using only instantaneous motion kinematics achieves an area under the precision-recall curve of 0.9, and secures a median time advance of 2.6 seconds to prevent potential collisions. Incorporating additional interaction patterns and contextual factors provides further performance gains. Across interaction scenarios such as rear-end, merging, and turning, GSSM consistently outperforms existing baselines in accuracy and timeliness. These results establish GSSM as a scalable, context-aware, and generalisable foundation to identify risky interactions before they become unavoidable, supporting proactive safety in autonomous driving systems and traffic incident management. Code and experiment data are openly accessible at https://github.com/Yiru-Jiao/GSSM.

翻译：准确且主动地向驾驶员或自动化系统预警即将发生的碰撞对道路安全至关重要，这在高度交互且复杂的城市环境中尤为关键。现有方法要么需要劳动密集型的稀疏风险标注，要么难以考虑多变的上下文因素，或者仅适用于有限场景。本文提出广义替代安全度量（GSSM），这是一种数据驱动方法，无需事故或风险标签即可从自然驾驶数据中学习碰撞风险。该方法在多个数据集上训练，并在2,591个真实世界事故和险发事故上评估，仅使用瞬时运动学的基本GSSM实现了精确率-召回率曲线下面积0.9，并获得了2.6秒的中位数提前预警时间以预防潜在碰撞。融入额外的交互模式和上下文因素可带来进一步的性能提升。在追尾、汇入、转弯等交互场景中，GSSM在准确性和及时性上均持续优于现有基线方法。这些结果确立了GSSM作为一个可扩展、上下文感知且可泛化的基础，能够在风险交互演变为不可避免之前识别它们，为自动驾驶系统和交通事件管理中的主动安全提供支持。代码与实验数据已在 https://github.com/Yiru-Jiao/GSSM 公开。