Abstract
This research is based on the system architecture of Edge Computing in the AIoT (Artificial Intelligence & Internet of Things) field. In terms of receiving data, the authors proposed approach employed the camera module as the video source, the ultrasound module as the distance measurement source, and then compile C++ with Raspberry Pi 4B for image lane analysis, while Jetson Nano uses the YOLOv3 algorithm for image object detection. The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load driving. The load drive has two parts: DC motor (rear wheel drive) and servo motor (front wheel steering). Then the final data of NodeMCU is wirelessly transmitted to the router, and then transmitted to the terminal smart device through the 4G network, thus completing the task of monitoring automatic driving. At present, artificial intelligence, the Internet of Things, and autonomous driving technologies are developing rapidly. The highlights of this research are three parts. The first part is the use of two image analysis methods to complete the automatic driving work, and the second part is the hardware architecture and circuit combination. The third part is that the system of this research is designed based on the Internet of Things architecture. The proposed system is to realize the data integration of various analysis platforms such as Raspberry Pi 4B, Jetson Nano, Motoduino U1, and mobile phone APP. The proposed method can not only realize the driving of an autonomous driving model car through image analysis but also monitor the autonomous driving process.
Highlights
In recent years, artificial intelligence, Internet of Things, and Self-Driving Car technology have flourished
The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load driving
The edge computing analysis method is completed, and our image analysis process is performed on the edge device
Summary
Artificial intelligence, Internet of Things, and Self-Driving Car technology have flourished. This research wants to work towards the second level of autonomous driving [2]. The current mainstream external sensors include cameras, lidars, ultrasonic radars, and positioning systems These sensors can provide huge driving environment data. It can instantly return the machine execution status, and enable testers to provide a more secure monitoring environment. Liu et al published the use of edge computing in the Internet of Things and 5G networks [13]. Donno et al compared IoT, cloud, fog, and edge computing in related research [14]. Goudarzi and others published edge computing under the framework of the Internet of Things [15]
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