Lidar Point Cloud compression, processing and learning for Autonomous Driving

Abbasi, Rashid, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0001-7595-2522, Alyamani, Hasan J, Amin, Farhan, Doh, Jaehyeok and Chen, Jianwen (2023) Lidar Point Cloud compression, processing and learning for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 24 (1). pp. 962-979. ISSN 1524-9050

Preview

Accepted Version
Available under License In Copyright.
Download (4MB) | Preview

Official URL: https://doi.org/10.1109/TITS.2022.3167957

Abstract

As technology advances, cities are getting smarter. Smart mobility is the key element in smart cities and Autonomous Driving (AV) are an essential part of smart mobility. However, the vulnerability of unmanned vehicles can also affect the value of life and human safety. In this paper, we provide a comprehensive analysis of 3D Point-Cloud (3DPC) processing and learning in terms of development, advancement, and performance for the AV system. 3DPC has recently attracted growing interest due to its extensive applications, such as autonomous driving, computer vision, and robotics. Light Detection and Ranging Sensors (LiDAR) is one of the most significant sensors in AV, which collects 3DPC that can accurately capture the outer surfaces of scenes and objects. Learning and processing tools in the 3DPC are essential for creating maps, perceptions, and localization devices in AV. The intention behind 3DPC learning and practical processing tools is to be considered the most essential modules to create, locate, and perceive maps in an AV system. The goal of the study is to know ``what has been tested in AV system so far and what is necessary to make it safer and more practical in AV system.'' We also provide insights into the necessary open problems that are required to be resolved in the future.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	03 Jan 2023 13:30
Publisher:	Institute of Electrical and Electronics Engineers
Additional Information:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Faculties > Science and Engineering
Subject terms:	Science & Technology, Technology, Engineering, Civil, Engineering, Electrical & Electronic, Transportation Science & Technology, Engineering, Transportation, Three-dimensional displays, Image coding, Laser radar, Real-time systems, Safety, Point cloud compression, Vehicular ad hoc networks, Self-driving cars, cybersecurity, 3D LiDAR data, object detection and tracking, vehicle safety, deep learning, EFFICIENT, 0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services, Logistics & Transportation
URI:	https://e-space.mmu.ac.uk/id/eprint/631072
DOI:	https://doi.org/10.1109/TITS.2022.3167957
ISSN	1524-9050
e-ISSN	1558-0016

Impact and Reach

Statistics

DownloadsShow export options

Activity Overview

6 month trend

992Downloads

6 month trend

311Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Repository staff only

Edit record