Edge Assisted Low-Latency Cooperative BEV Perception With Progressive State Estimation

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuhan Lin;Haoran Xu;Zhimeng Yin;Guang Tan
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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.
基于渐进式状态估计的边缘辅助低延迟协同BEV感知
现代智能汽车(IVs)配备了各种传感器和通信模块,增强了高级驾驶辅助系统(ADAS)的功能,并实现了车辆间的连接。本文主要研究多车协同感知,其主要目标是实现低延迟。该任务涉及附近的合作车辆将其摄像头数据发送到边缘服务器,然后将本地视图合并以创建全局交通视图。虽然多摄像头感知已经得到了积极的研究,但现有的解决方案往往依赖于深度学习模型,导致处理延迟过大。相反,我们建议利用机器人领域的状态估计技术来完成这项任务。我们明确地对系统状态进行建模和求解,解决了物体移动和视觉障碍带来的额外挑战。此外,我们还引入了一种渐进式状态估计管道来进一步加速系统状态通知,并辅以一种优化位置精度和感知平滑度的运动预测方法。实验结果表明,我们的方法优于深度学习方法,服务器处理延迟减少12.0到27.4倍,同时保持平均绝对误差低于1 m。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
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.
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