Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties as they arise in real-world scenarios. The resulting Risk Spot Detector (RSD) is applied and tested on naturalistic driving data of a multi-lane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.

翻译：风险评估是自动驾驶汽车开发与验证的核心要素，涵盖未来关键事件的发生概率与严重程度。时间车距和时间碰撞是常用的风险度量指标，且与发生概率存在定性关联。然而，这些指标缺乏理论推导，且仅针对特定类型交通场景（例如单车对间的跟驰行为）。本文基于生存分析理论提出一种概率性场景风险模型，并将其扩展至自然融入真实场景中出现的感知、时间与行为不确定性。所构建的风险点检测器在多交叉路口多车道主干道的自然驾驶数据上进行应用与测试，实现了道路风险图的可视化。相较于TH与TTC，本方法在风险预测方面更具选择性与特异性。RSD聚焦于高车辆密度的驾驶路段，该区域常出现大幅加速、减速或高速接近等操作。