In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.

翻译：本文通过定性和定量分析比较了三种基于模型的风险度量方法，并在真实纵向和交叉口场景数据集上进行了定量测试。我们从传统启发式方法——碰撞时间（TTC）入手，将其扩展至二维场景及非碰撞情形，得到最近接近时间（TTCE）。第二种风险度量方法采用高斯分布对位置不确定性进行建模，并利用空间占用概率计算碰撞风险。随后，我们基于稀疏关键事件的统计特性及所谓“生存条件”，推导出一种新型风险度量方法。该生存分析方法凭借其坚实的理论基础，在碰撞前期检测中表现出更早的预警能力，并在近碰撞与非碰撞场景中减少了误检。该方法可视为TTCE和高斯方法的广义化形式，适用于ADAS及自动驾驶系统的验证。