VistaScenario：面向搭载交通自动化智能系统的车辆的交互场景工程
VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation
AI总结：
针对现有方法难以处理以车辆交互为核心的场景分类标注问题，提出VistaScenario框架，结合时空场景演化树切片与Graph-DTW度量实现精细化场景处理，为智能车辆相关场景工程提供支撑。
摘要：
智能车辆与自动驾驶系统的智能与指标（I&I）、标定与认证（C&C）以及验证与确认（V&V）均依赖场景工程。为提取并索引场景，各类车辆交互行为值得高度关注，且需要精细化的描述与标注。然而现有方法无法很好地解决以车辆交互为核心的场景分类与标注问题。本文提出VistaScenario框架，用于为搭载交通自动化智能系统的车辆开展交互场景工程。基于总结出的车辆交互基本类型，我们通过时空场景演化树将场景数据流切分为一系列片段。我们还提出基于图计算树与动态时间规整的场景度量指标Graph-DTW，用于开展精细化的场景比对与标注，可高效过滤并提取极端交互场景与边缘案例。此外，基于自然驾驶场景数据集，在轨迹预测模型上的测试用例证明了该框架的有效性与优势。VistaScenario可为场景数据的使用与索引提供坚实支撑，进一步推动智能车辆与交通自动化的发展。
英文摘要：
Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.
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。
