Analysis of Distracted Driver Behaviour Using Self-Organizing Maps

被引：0

作者：

Samson, Matthew Immanuel ^{[1
]}

Calvert, David A. ^{[1
]}

Trick, Lana M. ^{[2
]}

机构：

[1] Univ Guelph, Sch Comp Sci, Guelph, ON, Canada

[2] Univ Guelph, Dept Psychol, Guelph, ON, Canada

来源：

2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2021年

关键词：

Self-Organizing Map; Distracted Driving; Feature Analysis;

D O I：

10.1109/CCWC51732.2021.9376083

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This work studies driving under different distractions and how they affect driver's behaviour. Driving patterns are studied by constructing Self Organizing Maps and training it on the data for multiple drivers. This results in a structure that maps each driver under a particular distraction to their behaviour. The map is then analyzed by labeling features from the datasets. These labels serve to examine different behaviour of each driver.

引用

页码：101 / 103

页数：3

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