Data Fusion in Infrastructure-Augmented Autonomous Driving System: Why? Where? and How?
Jianda Wang, Zhendong Wang, Bo Yu, Jie Tang, Shuaiwen Leon Song, Cong Liu, Yang Hu
Abstract
This article 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 enabled by the Internet of Things (IoT), named infrastructure-augmented autonomous driving (IAAD). We present an in-depth introduction to the IAAD hardware and software on both road side and vehicle side. We extensively characterize the IAAD system and observe that the network condition fluctuation along the road is the main roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed “interframe fusion” and “planning fusion” to complement the state-of-the-art “intraframe fusion.” We demonstrate that each fusion method has its own benefit and constraint. In order to select the best fusion method under varying network conditions, we propose “fusion criteria” to instruct the IAAD system to intelligently make the selection and implement a system framework named adaptive spatial-temporal (S–T) choice to realize the adaptive fusion guided by the “fusion criteria.” Our real-world field data verifies that S–T choice has significantly improved autonomous driving’s safety and reliability by decreasing the fusion miss ratio from 30% to 7% and remain the planning displacement error within the 1.7 m instead of 4 m when the network condition exacerbates.