The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

中文翻译：

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S-LDM：基于5G的集中式增强集体感知的服务器本地动态地图


汽车领域正在经历重大的技术进步，其中包括通过车辆网络（通常称为车对万物（V2X）通信）使下一代自动驾驶汽车变得更智能、更环保、更安全。与 V2X 一起，自动驾驶车辆的集中式机动管理服务变得越来越重要，因为它们可以对道路进行完整的观察，即使是针对 L4 级及以上驾驶的最复杂的机动也可以进行最佳管理。这些服务面临着严格要求高可靠性和低延迟的挑战，这些挑战可以通过在精心策划的多接入边缘计算（MEC）平台上的部署来解决。为了正确管理安全关键操作，这些服务需要从车辆接收大量数据，即使有用的数据子集通常与道路上的特定环境相关（例如，与特定道路使用者或地理区域相关） ）。对大量原始消息进行解码和后处理（然后大部分被过滤）会增加安全关键服务的负载，而这些服务应该专注于满足实际控制和管理操作的最后期限。在此基础上，我们提出了一种创新的开源、支持 5G 和 MEC 的服务，称为服务器本地动态地图 (S-LDM)。 S-LDM 是一项使用符合标准的消息收集有关车辆和其他非连接道路对象的信息的服务。其主要目的是创建一个集中式动态道路地图，可以在需要时与管理 L4 自动化的其他服务有效共享。 通过这样做，S-LDM 使这些服务能够广泛而准确地了解路段的当前状况，从而减轻它们快速处理大量消息的需要。在详细描述服务架构后，我们通过广泛的实验室和试点试验对其进行验证，涉及欧洲三大网络运营商的 MEC 平台和生产 5G 网络以及两辆配备 V2X 车载单元 (OBU) 的 Stellantis 车辆。我们展示了它如何有效地处理高更新率并在不到零点几微秒的时间内处理每条消息。我们还提供完整的可扩展性分析，其中包含部署选项的详细信息，提供有关在基于 5G 的 V2X 实际场景中应在何处创建新实例的见解。