Vol. 4 No. 2 (2024): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Driver behavior detection using intelligent algorithms

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  • NAIF ABDULRAHEEM MAHMOOD ALZEBARI,
  • YAŞAR BECERİKLİ
NAIF ABDULRAHEEM MAHMOOD ALZEBARI
Kocaeli Üniversity
Actual journals and last information about wireless communication

Published 31.07.2024

Keywords

  • ML models,
  • distracted,
  • aggressive,
  • drowsy,
  • angry,
  • fatigue,
  • PCA,
  • LDA
    • ...More
    Less

Copyright (c) 2024 NAIF ABDULRAHEEM MAHMOOD ALZEBARI; YAŞAR BECERİKLİ (Co-Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Driving in today's world is a very complicated and dangerous job that requires full attention. All types of be-havior, such as (feeling distracted, aggressive, drowsy, irritable, or tired, can divert the driver's attention away from the road). can lead to accidents and injuries. I can tell you that traffic ac-cidents are a serious problem worldwide. Because this incident is increasing in most countries of the world causing many vic-tims. The aim of this project is to employ machine learning (ML) methods to develop a system capable of identifying driver ac-tions and behaviors. Therefore, it is essential to identify risky driving behaviors such as distracted, aggressive, drowsy, irrita-ble, or tired driving. To achieve this goal, we are working on 15 driver behaviors in this project. We have categorized the pro-vided images using various ML models to determine whether the driver is driving safely or engaging in distracting activities or, aggressive, drowsy, irritable, or tired driving. Our approach in-volves comparing different models such as Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) to de-termine the best one based on the relevant metrics. The results indicate that. That shows higher precision, recall, F1, and accu-racy scores with LDA compared to PCA, especially methods Support Vector Machines (SVM), Bootstrap Aggregating (Bag-ging), and K-Nearest Neighbors (KNN), Also the results indicate that the combination of PCA and LDA can further enhance the performance of many of the models.

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