Converence: Artificial Neural Network Algorithm for Autonomous Vehicle Ultrasonic Multi-Sensor System

Suprapto, Bhakti Yudho and Nawawi, Zainuddin (2022) Converence: Artificial Neural Network Algorithm for Autonomous Vehicle Ultrasonic Multi-Sensor System. In: 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 2020, Malang, Indonesia.

Text (Paper Electronics, Communications, Controls and Informatics Seminar (EECCIS)) Artificial Neural Network Algorithm for Autonomous Vehicle Ultrasonic Multi-Sensor System.pdf Download (1MB)

Abstract

Abstract - Autonomous vehicles are vehicles that run automatically without a driver. Therefore, the vehicle requires sensors to detect surrounding objects to avoid collisions with other objects or vehicles. A variety of sensors can be used to support the system. One of the sensors used is an ultrasonic sensor that has the reliability and robustness to various light conditions and radio waves, especially when compared to camera sensors, radar and lidar. The recent implementation of ultrasonic sensors in vehicles is limited as a parking guide, so it needs to be developed for further functions, considering that ultrasonic sonar technology has advanced with even greater detection and long distances range. Hence, as a continuation of previous research in ultrasonic sensor characteristics, this paper carries out the application of artificial neural network algorithms that get input in the form of signals that refer to the output signal from the ultrasonic sensors, which have already assembled into a multi-sensor, which is 8 (eight) ultrasonic sensors positioned around the vehicle, two sensors in the front, two sensors in the rear and four sensors in the right and left side of vehicle. The sensors and algorithms will support the autonomous vehicle system, where if the sensors detect the obstructive objects, the system will provide an output in the form of a decision to make the braking order, soft braking, turning left, turning right, or staying run straight when the front sensors do not detect a barrier object. This is done in anticipation of an accident and avoid a collision. Each condition and decision will be determined by which sensor detects the barrier object. Input and output will be simulated using the tool of artificial neural network algorithms, so as to get the most optimal weight and low error rate.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Mr. Bhakti Suprapto
Date Deposited:	28 Apr 2023 16:14
Last Modified:	28 Apr 2023 16:14
URI:	http://repository.unsri.ac.id/id/eprint/97986

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