ConScenD数据集：根据OpenX中的ALKS法规UNECE R157，来自highD数据集的具体场景
The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX
摘要：
随着关于自动车道保持系统的UNECE R157法规的出台，第一个引入3级系统的乘用车框架将于2020年推出。根据最近的研究项目，包括学术界和汽车行业，该法规利用基于场景的测试进行安全评估。自动驾驶系统安全验证的复杂性需要系统级仿真。然而，该法规缺少测试用例生成所需的参数化。为了克服这个问题，我们将曝光，并考虑交通参与者的异质行为提取具体的情况下，根据法规的情况下的定义，从已建立的自然公路数据集highD。我们提出了一种方法来找到现实世界的数据中的场景，提取参数建模的情况下，并将它们转移到模拟。在此过程中，提取了340多个场景。生成OpenSCENARIO文件，以便将场景示例性地传输到CARLA和esmini。我们比较的轨迹，以检查模拟中的场景的相似性记录的情况下。为了促进研究，我们发布了名为ConScenD的结果数据集以及使用这两种模拟工具的说明。该数据集可在https://www.levelXdata.com/scenarios在线获得。
英文摘要：
With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first framework for the introduction of passenger cars with Level 3 systems has become available in 2020. In accordance with recent research projects including academia and the automotive industry, the Regulation utilizes scenario based testing for the safety assessment. The complexity of safety validation of automated driving systems necessitates system-level simulations. The Regulation, however, is missing the required parameterization necessary for test case generation. To overcome this problem, we incorporate the exposure and consider the heterogeneous behavior of the traffic participants by extracting concrete scenarios according to Regulation's scenario definition from the established naturalistic highway dataset highD. We present a methodology to find the scenarios in real-world data, extract the parameters for modeling the scenarios and transfer them to simulation. In this process, more than 340 scenarios were extracted. OpenSCENARIO files were generated to enable an exemplary transfer of the scenarios to CARLA and esmini. We compare the trajectories to examine the similarity of the scenarios in the simulation to the recorded scenarios. In order to foster research, we publish the resulting dataset called ConScenD together with instructions for usage with both simulation tools. The dataset is available online at https://www.levelXdata.com/scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
