@inproceedings{42efd4fb88b94fd5b0142cc130e0a0e7,
title = "Traffic Sign Detection for Navigation of Autonomous Car Prototype using Convolutional Neural Network",
abstract = "The Autonomous Car is one of Artificial Technology's innovations in the driving field. Many features of Autonomous Car which are using a sensor. One of its navigation feature which use camera sensor to navigate using the traffic sign. But this navigation feature, which uses Traffic Sign, is implausible to be found in Indonesia. In this paper, it will be developed a Detection System for Autonomous Car to navigate using the Traffic Sign, so Autonomous Car can navigate according to the Traffic Sign, which is detected with the Autonomous Car. Using a camera sensor and YOLO Deep Learning [1], Autonomous Car will recognize the Traffic Sign and classified Traffic Sign as with its function. From this classification, Autonomous Car will respond as the detected Traffic Sign means and will activate Autonomous Car's Actuator.",
keywords = "Actuator, Autonomous Car, Detection System, Traffic Sign, YOLO",
author = "Mohammed Ikhlayel and Iswara, {Adre Johan} and Arief Kurniawan and Ahmad Zaini and Yuniarno, {Eko Mulyanto}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 ; Conference date: 17-11-2020 Through 18-11-2020",
year = "2020",
month = nov,
day = "17",
doi = "10.1109/CENIM51130.2020.9297973",
language = "English",
series = "CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "205--210",
booktitle = "CENIM 2020 - Proceeding",
address = "United States",
}