Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles. Such services are usually deployed at the multi-access edge computing (MEC) to ensure low-latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this article, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to predict the vehicle density to be served by a MEC host and derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model, and hence the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application.

中文翻译：

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车辆防撞系统的最佳 MEC 资源规划：深度学习方法


车辆之间的碰撞检测和避免是车联网设想的关键服务之一。此类服务通常部署在多接入边缘计算（MEC）上，以确保低延迟通信，从而保证实时反应，避免车辆之间的碰撞。为了最大化道路的覆盖范围并确保所有车辆都连接到最佳的MEC主机（就地理位置而言），需要在所有MEC主机上实例化防撞应用程序。这可能会增加后者可用的计算资源的负担。在本文中，我们提出了一个人工智能驱动的框架，旨在优化 MEC 主机的计算资源。我们的框架使用深度学习来预测 MEC 主机所服务的车辆密度，并得出碰撞检测应用程序最佳运行所需的准确计算资源。我们使用代表大城市车辆流动性的真实数据集来评估所提出的框架。获得的结果显示了我们的预测模型的准确性，以及我们的资源分配框架的效率，以准确推断出应用程序的每个实例所需的最佳计算资源。