论文标题
交通现场的指纹：通用和独立场景评估的方法
Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment
论文作者
论文摘要
自动化车辆安全评估的主要挑战是确保所有交通参与者的风险尽可能低。一个在自动驾驶中越来越流行的概念是基于方案的测试。它建立在以下假设的基础上，即道路上的大多数时间都可以看作是不批判性的，并且主要在危急情况下有助于安全案件。描述关键性的指标对于从测量数据中自动确定临界情况和方案是必要的。但是，建立的指标缺乏普遍性或公制组合的概念。在这项工作中，我们提出了一个多维评估模型，该模型基于常规指标，可以独立于场景类型评估场景。此外，我们提出了两种新的，进一步增强的评估方法，可以作为通用指标。然后，使用Motion数据集中的实际数据评估和讨论我们介绍的指标。
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.
