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:在本文中,我们比较了三种不同的基于模型的风险评估措施,从质量上评价其强度和弱点,并以一系列真实的纵向和交叉情景进行定量测试。我们从传统的超时集合(TTC)开始,我们将其推广到2D操作和非崩溃(TTC),以检索时间到崩溃(TTCE),第二个风险计量模型将不确定性与高斯分布定位在一起,并使用空间占用概率来应对碰撞风险。然后,我们根据稀有的危急事件和所谓的生存条件的统计,得出一种新的风险评估措施。由此产生的生存分析显示,在近崩溃和非崩溃(TTCE)情况下,早期的发现时间是碰撞时间,在接近崩溃(TTCE)和非崩溃(TCE)的可靠理论基础支持下,不那么错误的正面检测是比较的。这可以被视为TCE和高斯方法的概括化,适合ADAS和AD的验证。</s>