Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time...

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Bibliographic Details
Published in:Electronics (Basel) 2020-12, Vol.9 (12), p.2084
Main Authors: Lee, Junwon, Lee, Kieun, Yoo, Aelee, Moon, Changjoo
Format: Article
Language:English
Subjects:
Algorithms
Automobiles
Autonomous cars
Autonomous vehicles
Big Data
Cloud computing
Data integrity
Data processing
Global positioning systems
GPS
High definition
Internet of Things
Lidar
Linux
Normal distribution
Real time
Sensors
Servers
Software
Three dimensional models
Vehicle safety
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Summary:Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9122084