AoI-centric Task Scheduling for Autonomous Driving Systems
Chengyuan Xu, Qian Xu, Jianping Wang, Kui Wu, Kejie Lu, Chunming Qiao
Abstract
An Autonomous Driving System (ADS) uses a plethora of sensors and many deep learning based tasks to aid its perception, prediction, motion planning, and vehicle control. To ensure road safety, those tasks should be synchronized and use the latest sensing data, which is challenging since 1) different sensors have different sensing periods, 2) the tasks are interdependent, 3) computing resource is limited. This work is the first that uses Age of Information (AoI) as the performance metric for task scheduling in an ADS. We show that minimizing AoI is equivalent to jointly minimizing the response time and maximizing the throughput. We formally formulate the AoI-centric task scheduling problem. To derive practical scheduling solutions, we extend the formulation and formulate the optimal AoI-centric periodic scheduling problem with a given cycle. A reinforcement learning-based solution is designed accordingly. With experiments simulated according to the Apollo driving system, we compare the scheduling performance of the AoI-centric task scheduling with Apollo’s schedulers from the perspective of AoI, throughput, and worst case response time. The experiment results show that the maximum AoI in the proposed scheduling solution with 4 cores is lower than that in Apollo’s schedulers with 8 cores.