{"title":"基于激光雷达的智能交通系统边缘检测方法","authors":"Yung-Yao Chen, Hsin-Chun Lin, Hao-Wei Hwang, K. Hua, Yu-Ling Hsu, Sin-Ye Jhong","doi":"10.1561/116.00000118","DOIUrl":null,"url":null,"abstract":"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)","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"1 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Edge Lidar-Based Detection Method in Intelligent Transportation System\",\"authors\":\"Yung-Yao Chen, Hsin-Chun Lin, Hao-Wei Hwang, K. Hua, Yu-Ling Hsu, Sin-Ye Jhong\",\"doi\":\"10.1561/116.00000118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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)\",\"PeriodicalId\":44812,\"journal\":{\"name\":\"APSIPA Transactions on Signal and Information Processing\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"APSIPA Transactions on Signal and Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1561/116.00000118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"APSIPA Transactions on Signal and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1561/116.00000118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
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)