インフラ拡張型自動運転における適応的データ融合の必要性
Brief Industry Paper: The Necessity of Adaptive Data Fusion in Infrastructure-Augmented Autonomous Driving System
路側インフラと車両を協調させる自動運転システムIAADの設計を概観し、ネットワーク変動に対応するため新たな融合手法を提案・比較した論文。
著者: Shaoshan Liu, Jianda Wang, Zhendong Wang, Bo Yu, Wei Hu, Yahui Liu, Jie Tang, Shuaiwen Leon Song, Cong Liu, Yang Hu
分類: cs.DC, cs.RO
原文アブストラクト
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.
