论文标题
根据实际驾驶数据确定具有挑战性的高速公路筛查以安全验证自动化车辆的安全验证
Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data
论文作者
论文摘要
为了成功推出自动车辆（AV），其安全证明至关重要。由于开放的参数空间，可能会发生无限数量的交通情况，这使得安全证明是未解决的问题。通过所谓的基于方案的方法，必须确定所有相关的测试方案。本文介绍了一种方法，该方法发现了实际驾驶数据（\ rddwo）中特别具有挑战性的情况，并使用新颖的指标评估了它们的难度。从HighD数据开始，使用层次聚类方法提取方案，然后使用基于规则的分类分配给九个预定义的功能方案之一。随后评估混凝土方案的特殊特征是它独立于测试工具的性能，因此对所有AV有效。以前的评估指标通常基于场景的关键性，但是，这取决于测试工具的行为，因此仅有条件地适合提前查找“良好”测试用例。结果表明，通过这种新方法，可以得出减少数量的特别具有挑战性的测试场景。
For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (\RDDwo) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.
