Efficient and Scalable Edge-Assisted Service Allocation for Connected Vehicles with Variable Velocities

In modern vehicular systems, substantial computation is required for functions such as high-definition mapping, object detection, and driving assistance. While onboard computing offers low-latency access, it is sometimes insufficient in capacity. Cloud computing provides scalability but suffers from latency issues. Edge computing offers a balance of low-latency, localized service delivery, and enhanced computational capacity, but it demands sophisticated allocation and delivery mechanisms. In this paper, we address the service allocation problem in edge-assisted vehicular networks under realistic assumptions of variable but bounded vehicle velocities. We design efficient computation allocation algorithms and introduce a speed-adjustment suggestion mechanism for vehicles not immediately assigned resources, allowing them to slow down and increase future allocation chances. Moreover, we propose a dedicated scheduling strategy tailored to normal traffic and platoon-based traffic, leveraging their structural properties. To mitigate computational overhead, we develop a novel and scalable approximation technique. Experimental evaluations across various traffic scenarios demonstrate the effectiveness, scalability, and real-time applicability of the proposed methods. Our code is available at https://github.com/AAAA-source/Edge-Assisted-Service-Allocation-for-Connected-Vehicles-with-Variable-Velocities .