An Adaptive Rank-Based Tensor Ring Completion Model for Intelligent Transportation Systems

Low rank tensor ring based data recovery algorithms have been widely used in vehicle road cooperation intelligent transportation system to recover missing data entries in the sensing data pre-processing stage. However, the existing tensor ring decomposition based methods often resolve the low rank optimization with predefined ranks, which often leads over-fitting when the selected rank is large. To overcome this challenge, we propose a Bayesian inference based tensor ring completion method which can automatically learn an optimal rank for the tensor ring completion. In this work, a likelihood Statistical model is firstly developed for low rank tensor ring approximation, and we impose a hierarchical sparse induced prior on the forward and horizontal slices of the kernel factor. Then, the Variational Bayesian algorithm is used to derive the parameters in the model, and the tensor ring rank can be achieved by gradually pruning the sparse horizontal and forward slice components in the factor. Finally, to elevate the proposal, numerous experiments have been conducted on two different intelligent transportation datasets, and the experimental results show that the proposed method can get the state-of-the-art performance in terms of recovery accuracy.

I. Introduction

With the advancements in computer communication technologies, the Vehicle road cooperation intelligent transportation system has become one of the most important research topics in the Internet of Vehicle (IoV) [1]. This field integrates computer vision, wireless sensing, machine learning, and IoT, and it use advanced wireless communication and next-generation Internet technologies to implement dynamic real-time information interaction between vehicles, vehicles and pedestrians, and vehicles and roads in an all-round way significantly and improves traffic driving experience by analyzing collecting traffic data such as driving state and road condition. However, due to sensor and wireless communication limitations, significant traffic data loss occurs during collection [2], which will seriously affects the final analysis results and thus leading wrong management strategies in the ITS applications [3]. Therefore, how to recover the missing data and generate reliable traffic data has been an important issue in recent Vehicle road cooperation ITS research.