基于激光雷达的智能交通系统边缘检测方法

IF 3.2 Q1 Computer Science
Yung-Yao Chen, Hsin-Chun Lin, Hao-Wei Hwang, K. Hua, Yu-Ling Hsu, Sin-Ye Jhong
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引用次数: 0

摘要

物联网技术突飞猛进。在物联网相关应用中,车联网(IoV)被认为是下一代智能交通系统不可或缺的基础设施。车联网要求车辆能够可靠地感知周围环境。研究人员特别关注激光雷达传感,因为它在极端天气下非常强大。然而,车联网传感数据在车辆和云之间传输,激光雷达需要大量的数据;因此,云计算的通信可能具有挑战性。为了解决这一困难,提出了一种基于激光雷达的车联网边缘节点检测方法。由于点云的稀疏性,激光雷达探测小目标非常困难。尽管一些研究人员尝试通过融合原始点云细节来解决这一问题,但现有的方法仍然降低了模型效率和内存成本,这并不适合车联网。为了克服这个问题,本文提出了一种新的模型来增强三维(3D)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Edge Lidar-Based Detection Method in Intelligent Transportation System
Rapid advances have occurred in Internet of Things technologies. Among Internet of Things–related applications, Internet of Vehicles (IoV) is regarded as integral infrastructure for next-generation intelligent transportation systems. IoV requires vehicles to perceive their surroundings reliably. In particular, researchers have focused on LiDAR sensing because it is robust in extreme weather. However, IoV sensing data are transmitted between vehicles and the cloud, and LiDAR requires a large quantity of data; thus, communication for cloud computing might be challenging. To address this difficulty, a LiDAR-based detection method for an IoV edge node is proposed. Small-object detection through LiDAR sensing is difficult because of the sparsity of point clouds. Although some researchers have attempted to solve this problem by fusing raw point cloud details, existing approaches still reduce model efficiency and memory cost, which is unsuitable for IoV. To overcome the problem, this paper proposes a novel model that enhances three-dimensional (3D)
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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