渐进式风险评估用于事故预判
Progressive Risk Estimation for Accident Anticipation
AI总结：
提出PRE-ACT框架，将事故风险建模为持续上升信号，通过时间顺序和距事故时间感知渐进提高风险并抑制误报，在MM-AU和Nexar上显著改进，并引入分离分数评估全局风险曲线。
AI中文摘要：
事故预判旨在识别碰撞前的异常驾驶线索，同时避免正常驾驶时的误报。现有方法通常将这一任务视为二元分类，关注事故是否发生而非何时发生。我们提出PRE-ACT框架，将事故风险建模为随碰撞临近而持续上升的信号。通过显式强制时间顺序和距事故时间感知，我们的方法渐进式提高风险，同时抑制过早警报，在MM-AU子集和Nexar上取得显著改进。我们进一步引入分离分数来评估预测风险曲线在局部时间窗口之外的全局行为。代码和可视化可在该https URL获取。
英文摘要：
Accident anticipation aims to recognize anomalous driving cues before a crash while avoiding false alarms during normal driving. Existing approaches typically formulate this task as binary classification, focusing on whether an accident will occur rather than when it will occur. We propose PRE-ACT, a framework that models accident risk as a continuously evolving signal that increases as the crash approaches. By explicitly enforcing temporal ordering and distance-to-accident awareness, our method progressively raises risk while suppressing premature alarms, leading to significant improvements on MM-AU subsets and Nexar. We further introduce a Separation Score to evaluate the global behavior of predicted risk curves beyond local temporal windows. Code and visualizations are available at https://github.com/giddyyupp/PRE-ACT.
