Traffic conflict detection is essential for proactive road safety by identifying potential collisions before they occur. Existing methods rely on surrogate safety measures tailored to specific interactions (e.g., car-following, side-swiping, or path-crossing) and require varying thresholds in different traffic conditions. This variation leads to inconsistencies and limited adaptability of conflict detection in evolving traffic environments. Consequently, a need persists for consistent detection of traffic conflicts across interaction contexts. To address this need, this study proposes a unified probabilistic approach. The proposed approach establishes a unified framework of traffic conflict detection, where traffic conflicts are formulated as context-dependent extreme events of road user interactions. The detection of conflicts is then decomposed into a series of statistical learning tasks: representing interaction contexts, inferring proximity distributions, and assessing extreme collision risk. The unified formulation accommodates diverse hypotheses of traffic conflicts and the learning tasks enable data-driven analysis of factors such as motion states of road users, environment conditions, and participant characteristics. Jointly, this approach supports consistent and comprehensive evaluation of the collision risk emerging in road user interactions. Our experiments using real-world trajectory data show that the approach provides effective collision warnings, generalises across distinct datasets and traffic environments, covers a broad range of conflict types, and captures a long-tailed distribution of conflict intensity. The findings highlight its potential to enhance the safety assessment of traffic infrastructures and policies, improve collision warning systems for autonomous driving, and deepen the understanding of road user behaviour in safety-critical interactions.

翻译：交通冲突检测对于主动道路安全至关重要，它能在潜在碰撞发生前进行识别。现有方法依赖于针对特定交互场景（如跟驰、侧向擦碰或路径交叉）定制的替代安全指标，且在不同交通条件下需要变化阈值。这种差异性导致冲突检测在动态变化的交通环境中存在不一致性和有限适应性。因此，跨交互场景实现一致的交通冲突检测需求持续存在。为应对这一需求，本研究提出一种统一的概率方法。该方法建立了交通冲突检测的统一框架，将交通冲突建模为道路使用者交互中依赖场景的极端事件。冲突检测进而被分解为一系列统计学习任务：表征交互场景、推断接近度分布、评估极端碰撞风险。该统一框架兼容多种交通冲突假设，而学习任务支持对道路使用者运动状态、环境条件和参与者特征等因素进行数据驱动的分析。综合而言，该方法支持对道路使用者交互中出现的碰撞风险进行一致且全面的评估。我们使用真实轨迹数据进行的实验表明：该方法能提供有效的碰撞预警，在不同数据集和交通环境中具有泛化能力，覆盖广泛的冲突类型，并能捕捉冲突强度的长尾分布。研究结果凸显了其在提升交通基础设施与政策安全评估、改进自动驾驶碰撞预警系统，以及深化对安全关键交互中道路使用者行为理解方面的潜力。