This study proposes a unified theory and statistical learning approach for traffic conflict detection, addressing the long-existing call for a consistent and comprehensive methodology to evaluate the collision risk emerging in road user interactions. The proposed theory assumes context-dependent probabilistic collision risk and frames conflict detection as assessing this risk by statistical learning of extreme events in daily interactions. Experiments using real-world trajectory data are conducted in this study, where a unified metric of conflict is trained with lane-changing interactions on German highways and applied to near-crash events from the 100-Car Naturalistic Driving Study in the U.S. Results of the experiments demonstrate that the trained metric provides effective collision warnings, generalises across distinct datasets and traffic environments, covers a broad range of conflicts, and delivers a long-tailed distribution of conflict intensity. Reflecting on these results, the unified theory ensures consistent evaluation by a generic formulation that encompasses varying assumptions of traffic conflicts; the statistical learning approach then enables a comprehensive consideration of influencing factors such as motion states of road users, environment conditions, and participant characteristics. Therefore, the theory and learning approach jointly provide an explainable and adaptable methodology for conflict detection among different road users and across various interaction scenarios. This promises to reduce accidents and improve overall traffic safety, by enhanced safety assessment of traffic infrastructures, more effective collision warning systems for autonomous driving, and a deeper understanding of road user behaviour in different traffic conditions.

翻译：本研究提出了一种用于交通冲突检测的统一理论与统计学习方法，以响应长期以来对评估道路使用者互动中碰撞风险的一致性与综合性方法的需求。该理论假设碰撞风险具有情境依赖性概率特征，并将冲突检测框架构建为通过对日常交互中极端事件的统计学习来评估此类风险。本研究利用真实轨迹数据开展实验，其中基于德国高速公路的变道交互数据训练了统一的冲突度量指标，并将其应用于美国100车自然驾驶研究中的近碰撞事件。实验结果表明：训练所得的度量指标能提供有效的碰撞预警，在不同数据集与交通环境中具有良好泛化能力，可覆盖广泛类型的冲突，并能输出冲突强度的长尾分布。基于这些结果反思，统一理论通过能涵盖不同交通冲突假设的通用化表述确保了评估的一致性；统计学习法则能全面考量道路使用者运动状态、环境条件及参与者特征等影响因素。因此，该理论与学习方法共同构建了一套可解释、可适配的方法论，适用于不同道路使用者和多样交互场景下的冲突检测。通过提升交通基础设施的安全评估水平、开发更有效的自动驾驶碰撞预警系统，以及深化对不同交通条件下道路使用者行为的理解，该方法有望降低事故发生率并提升整体交通安全水平。