Research on task-driven edge computing system in V2X scenarios

Abstract

In complex traffic scenarios, MEC (Mobile Edge Computing) servers need to compute and allocate autonomous driving tasks and communication resources in an efficient and timely manner. Under complex traffic flow, there are problems such as repeated computation, redundancy, and waste of sensory information required by vehicles with similar driving tasks when a single vehicle is used as the service object. To address this problem, our research constructs a task-driven MEC system that encodes driving task features based on position, velocity, and acceleration using the spatial association hypothesis and proposes an improved clustering grouping algorithm based on the task similarity of nodes in a traffic flow. To save the computational and communication resources of the server, we further propose a core vehicle election strategy for the optimal channel state and use spatial coordinate transformation to realize the sensory data sharing between the core vehicle and subsequent vehicles. The MEC server only needs to maintain simple data frame communication with all vehicle nodes to realize real-time sensing of the traffic flow topology. The task-driven edge computing system built in our study fully utilizes the computational power of the vehicle terminals themselves, the channel resources between vehicles, and the autonomous sensing capability of the vehicle sensors. Our research provides a solution to the problem of resource exhaustion and communication delays in high-density traffic flow scenarios, such as accident-induced congestion and complex intersections.