Distributed Computation Offloading in Autonomous Driving Vehicular Networks: A Stochastic Geometry Approach

With the advancement of the Internet of Vehicles (IoV), delay-sensitive vehicular applications have flourished. Among them, the autonomous driving technology is a focal point. For autonomous driving vehicles, efficiently and timely processing the ever-increasing data is critical. In real traffic scenes, the task-processing efficiency is closely related to the traffic flows. However, the traffic flow modeling is always ignored or considered roughly in the most existing studies. For this issue, a traffic model based on a stochastic geometry framework is proposed to simulate a real traffic environment of autonomous driving vehicles. To reduce the cost of processing tasks, a distributed computation offloading scheme based on mobile edge computing (MEC) is proposed by soliciting nearby vehicles and roadside units (RSUs) with rich computing resources. For the average cost minimization optimization problem, we divide the NP-hard problem into several sub-problems and take advantage of the Lagrange multiplier with KKT constraints to solve by optimizing task splitting ratios. We compare the proposed traffic model with some common ones and also consider the pros and cons of different computation offloading strategies. Simulation results show that the proposed strategy outperforms other benchmarks and the proposed modeling method is rational.