Collision Prediction Based on Vehicular Communication System | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 7, Volume 22, Issue 3, September 2022, Pages 72-80 PDF (1.03 M) | ||
| Document Type: Review Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.22.3.7 | ||
| Authors | ||
| Abdulqader Falhi Jabbar* 1; Rana Fareed Ghani2; Asia Ali Salman3 | ||
| 1computer science, university of technology, Baghdad, Iraq | ||
| 2Department of Computer Science, University of Technology, Iraq-Baghdad | ||
| 3Computer Sciences Department, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction communication mechanism that alerts drivers to any autos that may be overtaking or bypassing the targeted vehicle. The primary goal of this paper is to leverage modern Internet of Things (IoT) and wireless sensor technologies to predict any potential accident that may occur as a result of car accidents. This paper proposes the Collision Prediction of a Moving Vehicle (CPMV) system. The information acquired by CPMV will alert the driver to divert the vehicle in a reasonable amount of time before any harm occurs. It redirects the inbound object that emitted the Ultrasound signal which was received by the vehicle, to a safe location. The proposed system predicts collision between vehicles through Wi-Fi and Bluetooth, using a set of sensors with a precision of 360 degrees and a distance of collision prediction of one meter and at a speed of 200-300 revolutions per minute. The python programming language was utilized to code the programs that control the vehicle during the implementation of this project. The Raspberry Pi 4 is utilized as the controller to examine the vehicle’s spatial data. The test results showed that using this application to deal with an approaching object can be a successful strategy in the three proposed scenarios at different angles and directions. | ||
| Keywords | ||
| collision prediction; collision avoidance; Internet of Things (IoT); Raspberry pi; vehicle communication | ||
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