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Brief Industry Paper: The Necessity of Adaptive Data Fusion in Infrastructure-Augmented Autonomous Driving System

Authors :
Liu, Shaoshan
Wang, Jianda
Wang, Zhendong
Yu, Bo
Hu, Wei
Liu, Yahui
Tang, Jie
Song, Shuaiwen Leon
Liu, Cong
Hu, Yang
Source :
28th IEEE Real-Time and Embedded Technology and Applications Symposium, 2022
Publication Year :
2022

Abstract

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communication platforms. We extensively characterize the IAAD system in the context of real-world deployment scenarios and observe that the network condition that fluctuates along the road is currently the main technical roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed "inter-frame fusion" and "planning fusion" to complement the current state-of-the-art "intra-frame fusion". We demonstrate that each fusion method has its own benefit and constraint.

Details

Database :
arXiv
Journal :
28th IEEE Real-Time and Embedded Technology and Applications Symposium, 2022
Publication Type :
Report
Accession number :
edsarx.2207.00737
Document Type :
Working Paper