@article{M709E2989,
title = "Performance Evaluation Using Neural Network Learning of Indoor Autonomous Vehicle Based on LiDAR",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2023",
issn = "2287-5891",
doi = "https://doi.org/10.3745/KTCCS.2023.12.3.93",
author = "Yonghun Kwon/Inbum Jung",
keywords = "Indoor Autonomous Driving, Neural Network, LiDAR, Raspberry Pi",
abstract = "Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication
process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor
self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors.
An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes
a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts
the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed
six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an
autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation.
Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the
number of input data on performance was confirmed."
}