Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/6104
Title: Collision avoidance with machine learning
Authors: Gharib, Saeed Al
Advisors: Karam, Walid 
Keywords: Machine Learning, backpropagation algorithm, fatal car accident, speeding, alcohol consumption
Subjects: Machine learning
Accidents--Prevention
University of Balamand--Dissertations
Dissertations, Academic
Issue Date: 2022
Abstract: 
The top four causes of deadly car accidents are speeding, drinking while operating a vehicle, driver weariness, and distracted driving. Speeding has the highest percentage among all factors that cause deadly car accidents. As cars become more automated, there is hope that a reduction in road fatalities will be observed. The goal of the study is to research and develop machine-learning algorithms to decrease car accidents and improve human safety on roads. The recent systems have achieved good results in which they were able to provide safer rides than human-controlled cars. Such systems used sensors and cameras that helped to eliminate human error. However, the behavior of these systems under various circumstances remains understudied until now. Additionally, these systems are not advanced enough to detect and moderate every possible impact. Therefore, the purpose of this paper is to apply a Machine learning backpropagation algorithm based on six different features (speed of the car, speed of the neighbor car, level of alcohol in blood, weather condition, road condition, and whether the brakes are applied). These features are used to train the system to predict the car accident and thus avoid a car crash.
Description: 
Includes bibliographical references (p. 23-24)
URI: https://scholarhub.balamand.edu.lb/handle/uob/6104
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Thesis
Appears in Collections:UOB Theses and Projects

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