Edge intelligence, which is a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers,i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

翻译：利用网络边缘的计算资源来加速人工智能应用的新模式,即智能智能(AI)应用,可以用来改进智能运输系统(ITS)。然而,由于物理限制和能源供应限制,边缘设备的计算能力通常有限。高海拔平台站(HAPS)的计算可被视为边缘计算的一个大有希望的延伸。HAPS部署在平流层,以提供广泛的覆盖面和强大的计算能力。它适合于协调地面资源并存储与ITS应用相关的基本数据。在这项工作中,三个计算层,即车辆、地面网络边缘和HAPS被整合在一起,为ITS建立一个计算框架,HAPS数据图书馆储存了这些应用所需的基本数据。此外,还引入了网络边缘技术,储存HAPS的一些基本数据,以便减少大规模的传播延迟。我们的目标是通过优化计算卸载和缓冲决定以及带宽度和计算资源分配来尽量减少系统的延迟。模拟结果突出了HAPS计算对减轻延误的好处,以及网络边缘缓冲的重要性。