A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly popular for testing in automated driving is scenario-based testing. It is founded on the assumption that most time on the road can be seen as uncritical and in mainly critical situations contribute to the safety case. Metrics describing the criticality are necessary to automatically identify the critical situations and scenarios from measurement data. However, established metrics lack universality or a concept for metric combination. In this work, we present a multidimensional evaluation model that, based on conventional metrics, can evaluate scenes independently of the scene type. Furthermore, we present two new, further enhanced evaluation approaches, which can additionally serve as universal metrics. The metrics we introduce are then evaluated and discussed using real data from a motion dataset.
翻译:自动车辆安全评估的一个主要挑战是确保所有交通参与者的风险尽可能低,在自动驾驶测试中日益流行的一种概念是以假设情况为基础的测试,其依据是假设多数在路上的时间可以被视为不批评的,而且主要在危急情况下有助于安全案件。描述临界度的尺度对于从测量数据中自动确定危急情况和假设情况是必要的。然而,既定的衡量标准缺乏普遍性或衡量组合概念。在这项工作中,我们提出了一个多层面的评价模型,根据常规指标,可以对场景进行独立于场景类型的评估。此外,我们提出了两种新的、进一步强化的评价方法,这些方法还可以作为通用的衡量标准。然后,利用运动数据集的实际数据来评估和讨论我们提出的衡量标准。