Affiliations: [a] Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China
| [b] Department of Mathematics and Information Engineering, Longdong University, Qingyang, China
| [c] School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
| [d] Baotou Teachers’ College, Baotou, China
Correspondence:
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Corresponding author. Xiumei Fan, Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China. E-mail: [email protected].
Abstract: Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost.
Keywords: Communication link lifetime prediction, partial offloading decision, machine learning, autonomous driving
