{"title":"Edge Assisted Low-Latency Cooperative BEV Perception With Progressive State Estimation","authors":"Yuhan Lin;Haoran Xu;Zhimeng Yin;Guang Tan","doi":"10.1109/TMC.2024.3509716","DOIUrl":null,"url":null,"abstract":"Modern intelligent vehicles (IVs) are equipped with a variety of sensors and communication modules, empowering Advanced Driver Assistance Systems (ADAS) and enabling inter-vehicle connectivity. This paper focuses on multi-vehicle cooperative perception, with a primary objective of achieving low latency. The task involves nearby cooperative vehicles sending their camera data to an edge server, which then merges the local views to create a global traffic view. While multi-camera perception has been actively researched, existing solutions often rely on deep learning models, resulting in excessive processing latency. In contrast, we propose leveraging the <italic>state estimation</i> technique from the robotics field for this task. We explicitly model and solve for the system state, addressing additional challenges brought by object mobility and vision obstruction. Furthermore, we introduce a <italic>progressive state estimation</i> pipeline to further accelerate system state notifications, supported by a motion prediction method that optimizes position accuracy and perception smoothness. Experimental results demonstrate the superiority of our approach over the deep learning method, with 12.0 × to 27.4 × reductions in server processing delay, while maintaining mean absolute errors below 1 m.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3346-3358"},"PeriodicalIF":7.7000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10772318/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Modern intelligent vehicles (IVs) are equipped with a variety of sensors and communication modules, empowering Advanced Driver Assistance Systems (ADAS) and enabling inter-vehicle connectivity. This paper focuses on multi-vehicle cooperative perception, with a primary objective of achieving low latency. The task involves nearby cooperative vehicles sending their camera data to an edge server, which then merges the local views to create a global traffic view. While multi-camera perception has been actively researched, existing solutions often rely on deep learning models, resulting in excessive processing latency. In contrast, we propose leveraging the state estimation technique from the robotics field for this task. We explicitly model and solve for the system state, addressing additional challenges brought by object mobility and vision obstruction. Furthermore, we introduce a progressive state estimation pipeline to further accelerate system state notifications, supported by a motion prediction method that optimizes position accuracy and perception smoothness. Experimental results demonstrate the superiority of our approach over the deep learning method, with 12.0 × to 27.4 × reductions in server processing delay, while maintaining mean absolute errors below 1 m.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.