Classifying Driver Attention Level Using Logistic Regression and Support Vector Machine

Ana Farhat; Ka C Cheok

doi:10.29007/gr9w

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Classifying Driver Attention Level Using Logistic Regression and Support Vector Machine

9 pages•Published: June 9, 2021

Ana Farhat and Ka C Cheok

Abstract

One of the major reasons of road related accidents is driver distraction. The aim of this study is to classify driver attention level which can be extended to improving driver warning systems by generating adaptive driver alert warning systems. In this paper logistic regression (LR) and support vector machine (SVM) classifiers are used to classify driver attention level. The performance of mentioned classifiers is illustrated and compared via the figures of predicted decision boundaries. Also, in comparison to LR, higher accuracy of SVM has been verified.

Keyphrases: driver attention classification, logistic regression, Support Vector Machine

In: Yan Shi, Gongzhu Hu, Takaaki Goto and Quan Yuan (editors). CAINE 2020. The 33rd International Conference on Computer Applications in Industry and Engineering, vol 75, pages 32--40

Links:	https://easychair.org/publications/paper/Xd94
	https://doi.org/10.29007/gr9w

BibTeX entry

@inproceedings{CAINE2020:Classifying_Driver_Attention_Level,
  author    = {Ana Farhat and Ka C Cheok},
  title     = {Classifying Driver Attention Level Using Logistic Regression and Support Vector Machine},
  booktitle = {CAINE 2020. The 33rd International Conference on Computer Applications  in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Takaaki Goto and Quan Yuan},
  series    = {EPiC Series in Computing},
  volume    = {75},
  pages     = {32--40},
  year      = {2021},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/Xd94},
  doi       = {10.29007/gr9w}}

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