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

Junwon Lee,Changjoo Moon,Kieun Lee,Aelee Yoo

doi:10.3390/electronics9122084

Abstract

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.

Highlights

The Internet of Things (IoT) is a network of objects with embedded sensors for exchanging data over the Internet [1]
For quantitative evaluation of the established system, the processing time of the system according according to the increase in processing data, the processing resources required for HD map to thegeneration, increase in processing data, the processing resources required for HD map generation, and the and the change in processing speed according to the increase in data size were evaluated
We propose a system that processes point cloud data obtained from multiple autonomous vehicles (AVs) in real time using the edge-fog-cloud-based computing environment and stores the generated large-scale

Summary

Introduction

The Internet of Things (IoT) is a network of objects with embedded sensors for exchanging data over the Internet [1]. IoT is utilized in various areas, including industrial and infrastructure systems, in addition to personal environments. IoT technology is leveraged in self-driving vehicles (i.e., autonomous vehicles, AVs). Self-driving vehicles collect driving environment data using various systems, such as cameras, light detection and ranging (LiDAR), radars, and the global positioning system (GPS), and can operate as an IoT edge computer. Research Institute, an AV produces data at a rate of approximately 4 TB/h. Within the 5–10 years, 90% of US cars are expected to be replaced by self-driving cars with 1–3 levels of autonomous driving functionality, and the amount of data generated is expected to increase further [2]

Methods

Results

Conclusion