Vehicle-Road-Cloud Collaborative Perception Framework and Key Technologies: A Review | IEEE Journals & Magazine | IEEE Xplore

Vehicle-Road-Cloud Collaborative Perception Framework and Key Technologies: A Review


Abstract:

Over recent years, the Vehicle-Road-Cloud Integration System (VRCIS) and Intelligent and Connected Vehicles (ICVs) have gained significant attention in the realm of auton...Show More

Abstract:

Over recent years, the Vehicle-Road-Cloud Integration System (VRCIS) and Intelligent and Connected Vehicles (ICVs) have gained significant attention in the realm of autonomous driving. By sharing data across diverse traffic participants and coordinating with VRCIS, ICVs can achieve enhanced perception accuracy and superior driving decisions, surpassing autonomous vehicles that rely solely on onboard sensors. Existing literature explores VRCIS’ overall architecture, applications, and deployment status. However, there is a lack of a comprehensive review focusing on the overarching architecture of ICV’s perception and its associated technologies, which are fundamental to VRCIS from an information integration perspective. This gap hinders the development of a robust perception framework for VRCIS, including the crucial perception technologies specific to it. This survey seeks to bridge this gap by offering an exhaustive review of the designed VRCIS perception framework and its specific perception technologies. Firstly, an overview of VRCIS’ perception architecture is provided, and the application relationships among various perception technologies are elucidated. Then, single-node, multi-node, and vehicle-road-cloud collaborative perception technologies are explored in sequence. Finally, the survey concludes with a discussion of insights and prospective future directions for VRCIS.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 25, Issue: 12, December 2024)
Page(s): 19295 - 19318
Date of Publication: 30 September 2024

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I. Introduction

Vehicles equipped predominantly with single-vehicle intelligence encounter numerous challenges in perception, decision-making, and control. For example, from the perception perspective, perception blind spots exist due to the limited field of view inherent in individual vehicles. Such limitations significantly impede the progression of autonomous driving from assisted stages to fully driverless stages. Vehicle-Road-Cloud Integration System (VRCIS) is considered to have the potential to promote autonomous driving to the advanced stage. VRCIS utilizes cutting-edge information and communication technologies to integrate the physical and cyber layers into a unified system, encompassing elements such as pedestrians, vehicles, roads, and the cloud. By facilitating unified perception, decision-making, and control, VRCIS aims to comprehensively enhance vehicle performance, traffic safety, and transportation system operational efficiency [1].

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