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Design of V2X-Based Vehicular Contents Centric Networks for Autonomous Driving

Authors
Lee, SanghoonJung, YounghwaPark, Young-HoonKim, Seong-Woo
Issue Date
Aug-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Autonomous vehicles; Vehicle-to-everything; 3GPP; Computer architecture; Roads; Vehicle dynamics; Safety; Autonomous driving; cooperative perception; C-V2X; HD map; vehicular content centric networks
Citation
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.23, no.8, pp.13526 - 13537
Journal Title
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume
23
Number
8
Start Page
13526
End Page
13537
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/151420
DOI
10.1109/TITS.2021.3125358
ISSN
1524-9050
Abstract
Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEE's Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPP's New Radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this paper, we define and design a vehicular network architecture compliant with 5G core networks to enable and support autonomous driving. As a validation example, a high-definition map needs to contain the context of trajectory for localization and planning of autonomous driving vehicles. We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of the proposed method via network simulations and our autonomous driving vehicle on the road. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.
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