Computation Offloading Strategy for Autonomous Vehicles

Vehicular edge computing is a progressing technology which provides processing resources to the internet of vehicles using the edge servers deployed at roadside units. Vehicles take advantage by offloading their computationintensive tasks to this infrastructure. However, concerning time-sensitive applications and the high mobility of vehicles, cost-efficient task offloading is still a challenge. This paper establishes a computation offloading strategy based on deep Q-learning algorithm for vehicular edge computing networks. To jointly minimize the system cost including offloading failure rate and the total energy consumption of the offloading process, the vehicle tasks offloading problem is formulated as a multiobjective optimization problem. Simulation results are presented to verify that the proposed algorithm can effectively reduce the offloading failure rate and the total energy consumption. Compared to Random and SeV Only algorithms, our proposition respectively shows a 10% and 22% reduction in failure rate, and 80% and 86% of reduced energy consumption.