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 emerged in road user interactions. The proposed theory assumes a context-dependent probabilistic collision risk and frames conflict detection as estimating the risk by statistical learning from observed proximities and contextual variables. Three primary tasks are integrated: representing interaction context from selected observables, inferring proximity distributions in different contexts, and applying extreme value theory to relate conflict intensity with conflict probability. As a result, this methodology is adaptable to various road users and interaction scenarios, enhancing its applicability without the need for pre-labelled conflict data. Demonstration experiments are executed using real-world trajectory data, with the unified metric trained on lane-changing interactions on German highways and applied to near-crash events from the 100-Car Naturalistic Driving Study in the U.S. The experiments demonstrate the methodology's ability to provide effective collision warnings, generalise across different datasets and traffic environments, cover a broad range of conflicts, and deliver a long-tailed distribution of conflict intensity. This study contributes to traffic safety by offering a consistent and explainable methodology for conflict detection applicable across various scenarios. Its societal implications include enhanced safety evaluations of traffic infrastructures, more effective collision warning systems for autonomous and driving assistance systems, and a deeper understanding of road user behaviour in different traffic conditions, contributing to a potential reduction in accident rates and improving overall traffic safety.

翻译：本研究提出了一种交通冲突检测的统一理论与统计学习方法，旨在响应长期以来对评估道路使用者互动中碰撞风险的一致且全面方法的需求。所提出的理论假设存在一种依赖于情境的概率性碰撞风险，并将冲突检测框架定义为通过从观测到的接近度与情境变量中进行统计学习来估计该风险。该方法整合了三个主要任务：从选定可观测变量中表征互动情境、推断不同情境下的接近度分布，以及应用极值理论将冲突强度与冲突概率相关联。因此，该方法能适应不同的道路使用者和互动场景，无需预先标记的冲突数据即可增强其适用性。演示实验使用真实轨迹数据执行，其中统一度量在德国高速公路的换道互动数据上进行训练，并应用于美国100-Car自然驾驶研究中的近碰撞事件。实验表明，该方法能够提供有效的碰撞预警，在不同数据集和交通环境中具有良好泛化能力，覆盖广泛的冲突范围，并产生冲突强度的长尾分布。本研究通过提供一种适用于多种场景、一致且可解释的冲突检测方法，为交通安全做出贡献。其社会意义包括：增强交通基础设施的安全评估，为自动驾驶和驾驶辅助系统提供更有效的碰撞预警系统，深化对不同交通条件下道路使用者行为的理解，从而有助于潜在降低事故率并提升整体交通安全水平。