Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.

中文翻译：

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在C-ITS方案中部署空间流查询应答

协作式智能运输系统（C-ITS）在通过车辆与基础设施之间基于V2X的通信提供收集和交换时空数据的手段方面发挥着重要作用，这将成为（半）道路安全的主要推动力。自动驾驶汽车。本地动态地图（LDM）是在空间上下文中集成静态和流数据的关键概念。LDM已在语义上进行了增强，以允许由移动性本体捕获的复杂域模型，以及针对满足语义概念和空间关系的数据流的查询。我们用于语义增强的方法是在本体介导的查询应答（OQA）的上下文中，并且具有基于DL-LiteA本体的联合查询，该查询支持流和空间对象之间的空间关系上的窗口运算符。在本文中，我们将展示如何在三种C-ITS场景交通统计，交通事件检测和高级驾驶辅助系统中扩展这种方法，以解决更广泛的用例。我们为上述用例定义了从必要的特定于域的功能派生的需求，并基于这些需求报告了查询语言和本体模型的扩展。这些扩展包括时间关系，数值预测和轨迹预测以及诸如缓存之类的优化策略。已使用实际交通模拟工具PTV Vissim对反映需求的查询进行了实验评估。它为我们在新场景中采用该方法的可行性/效率提供了证据。