Open AccessImage Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence
Author(s) -
Byoungman An,
Youngseop Kim
Publication year - 2022
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.21215
Subject(s) - computer science , ethernet , automotive industry , latency (audio) , image processing , real time computing , standardization , algorithm , embedded system , artificial intelligence , image (mathematics) , computer network , operating system , engineering , telecommunications , aerospace engineering
This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio Video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in real time.