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.

本文提出了基于生存分析考虑的概率情况风险模型，并将其扩展到自然地结合感性、时间和行为不确定性的真实场景。结果风险点检测器 (RSD) 应用于真实驾驶数据, 显示了路面重要性地图, 相较于 Time Headway (TH) 和 Time-To-Contact (TTC), 我们的方法在预测风险方面更具有选择性和特异性。

自然驾驶情境下的概率不确定性感知危险路段探测器