APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 4

An Edge Lidar-Based Detection Method in Intelligent Transportation System

Yung-Yao Chen, National Taiwan University of Science and Technology, Taiwan, Hsin-Chun Lin, National Taiwan University of Science and Technology, Taiwan, Hao-Wei Hwang, National Taiwan University of Science and Technology, Taiwan, Kai-Lung Hua, National Taiwan University of Science and Technology, Taiwan, Yu-Ling Hsu, National Taiwan University of Science and Technology, Taiwan, Sin-Ye Jhong, National Cheng Kung University, Taiwan, n98081034@ncku.edu.tw
 
Suggested Citation
Yung-Yao Chen, Hsin-Chun Lin, Hao-Wei Hwang, Kai-Lung Hua, Yu-Ling Hsu and Sin-Ye Jhong (2023), "An Edge Lidar-Based Detection Method in Intelligent Transportation System", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 4, e104. http://dx.doi.org/10.1561/116.00000118

Publication Date: 14 Aug 2023
© 2023 Y.-Y. Chen, H.-C. Lin, H.-W. Hwang, K.-L. Hua, Y.-L. Hsu and S.-Y. Jhong
 
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In this article:
Introduction 
Related Works 
Methodology 
Experiments 
Conclusion 
References 

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) structural information for preserving the details of voxel features while maintaining the high efficiency and low memory usage of voxel-based methods. Experimental results indicate that the proposed method outperforms state-of-the-art LiDAR-based 3D detection methods on the widely used KITTI dataset and achieves competitive performance for all classes.

DOI:10.1561/116.00000118

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APSIPA Transactions on Signal and Information Processing Special Issue - Emerging AI Technologies for Smart Infrastructure
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