RSU-assisted Proactive Perception and Edge Computing for Autonomous Driving.

Nowadays, autonomous driving is one of promising technologies in Internet of Vehicles. However, traditional environment sensing technologies via vehicle-mounted sensors are unable to provide sufficient information due to limited perception capabilities. The emerging solution based on Road-side Units (RSUs) for perceiving environment actively to assist autonomous driving can be expected in the foreseeable future. Specifically, RSUs are deployed to proactively acquire information of the environment. The sensing tasks are processed in an edge computing architecture. Finally, the results are sent to vehicles for reference to autonomous driving. One of its issues is task offloading, that is, how to assign computing tasks from RSUs to either the cloud, the vehicles of autonomous driving, or the local. In this article, we propose a novel strategy for offloading tasks of environment perception. To solve the problem, we formulate task offloading as a 0-1 mathematical model with the objective of minimizing the task delay. Due to the dynamics of the environment, we first present the problem as Markov Decision Process, and a reinforcement learning based approach is provided. Extensive simulation results demonstrate that our strategy can effectively reduce the long-term average delay of the tasks.