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Real-Time Control Using Convolution Neural Network for Self-Driving Cars
Author(s)
Dangskul, Woraphicha
Phattaravatin, Kunanon
Rattanaporn, Kiattisak
Kidjaidure, Yuttana
Date Issued
April 1, 2021
Type
Conference Paper
Abstract
In this paper, we perform an Autonomous deep learning robot using an end-to-end system. The system operates as the controller for navigating and driving automatically. The deep learning robot used Convolution Neural Network (CNN). The CNN architecture is Mobile net with Softmax activation function. The Softmax activation function predicts the probability of steering angles. In the training phase, the CNN model learns from images and steering angles that are collected during the driving. In the testing phase, we apply the diversified environment to the trained CNN model. The CNN model accuracy is up to 85.03%. The results showed that the CNN is able to learn the diversified tasks of lanes and roads following with and without lane marking, direction planning and automatically control. Also, the CNN can replace the conventional PID controller.
Citation
2021 7th International Conference on Engineering Applied Sciences and Technology Iceast 2021 Proceedings, 125-128, 2021