Key Perception Technologies for Intelligent Docking in Autonomous Modular Buses - Ye Xiao; Yuxuan Zheng; Xin Liu; Yifen Ye (Journal of Advanced Transportation)
Autonomous modular buses (AMBs) constitute a novel form of public transportation, enabling real-time adjustments of module configurations and facilitating passenger exchanges in transit. This approach resolves unpleasant transfer experiences and offers a potential solution to traffic congestion. However, while most existing research concentrates on logistical operations, the technical implementation of AMBs remains underexplored. This paper fills this gap by proposing key perception technologies for the docking process of AMBs, which presents a suite of sensors and segments the docking process into four stages. A late fusion-based perception network, featuring event-driven and periodic modules, is introduced to optimize perception by integrating multisource data. Plus, we suggest a “mutual view and coview” strategy to enhance perception accuracy in the unique scenario of docking. Experimental results demonstrate that our method achieves a substantial reduction of errors in x and y axes, as well as the heading angle compared with other state-of-the-art perception methods. Our research lays the groundwork for advancements in the precise docking of AMBs, offering promising tactics for other intelligent vehicle applications.