A scheduling algorithm for autonomous driving tasks on mobile edge computing servers

Autonomous driving has received widespread attention in recent years, while the limited battery life and computing capability of autonomous vehicles cannot support some necessary computation-intensive and urgent tasks with strict response time requirements. The results of the tasks would be useless and may cause serious safety hazards if the given time constraints are exceeded. On the other side, mobile edge computing (MEC) offers the possibility of autonomous vehicles to complete these time-constraint tasks due to its proximity and strong computing capabilities, with the faster 5G wireless networks to enable vehicles and MEC servers to exchange data in milliseconds. Then, it is a key issue to make the MEC servers to execute and complete these time-constraint autonomous-driving tasks as many as possible. So, we propose a task scheduling algorithm that can consider characteristics of autonomous-driving tasks and select more suitable MEC servers with task migration, based on an improved earliest deadline first algorithm through the replacement and recombination of tasks. From the experimental results, it can be concluded that the algorithm can schedule more tasks and benefit the urgent tasks effectively with the increase of the task amounts.