Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

This study was designed to gain insight into the influence of performing different types of secondary task while driving on driver eye movements and to build a safety evaluation model for secondary task driving. Eighteen young drivers were selected and completed the driving experiment on a driving s...

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Main Authors: Lisheng Jin, Huacai Xian, Yuying Jiang, Qingning Niu, Meijiao Xu, Dongmei Yang
Format: Article
Language:English
Published: SAGE Publishing 2014-01-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1155/2014/624561
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spelling doaj-f802716b47fb4a1e85386e23bdef22422020-11-25T02:48:48ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322014-01-01610.1155/2014/62456110.1155_2014/624561Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye MovementsLisheng Jin0Huacai Xian1Yuying Jiang2Qingning Niu3Meijiao Xu4Dongmei Yang5 Transportation College, JiLin University, ChangChun 130024, China Transportation College, JiLin University, ChangChun 130024, China China-Japan Union Hospital of JiLin University, ChangChun 130033, China Transportation College, JiLin University, ChangChun 130024, China Transportation College, JiLin University, ChangChun 130024, China Transportation College, JiLin University, ChangChun 130024, ChinaThis study was designed to gain insight into the influence of performing different types of secondary task while driving on driver eye movements and to build a safety evaluation model for secondary task driving. Eighteen young drivers were selected and completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks were analyzed. Based on measures which had significant difference between the baseline and secondary tasks driving conditions, the evaluation index system was built. Method of principal component analysis (PCA) was applied to analyze evaluation indexes data in order to obtain the coefficient weights of indexes and build the safety evaluation model. Based on evaluation scores, the driving safety was grouped into five levels (very high, high, average, low, and very low) using K -means clustering algorithm. Results showed that secondary task driving severely distracts the driver and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.https://doi.org/10.1155/2014/624561
collection DOAJ
language English
format Article
sources DOAJ
author Lisheng Jin
Huacai Xian
Yuying Jiang
Qingning Niu
Meijiao Xu
Dongmei Yang
spellingShingle Lisheng Jin
Huacai Xian
Yuying Jiang
Qingning Niu
Meijiao Xu
Dongmei Yang
Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
Advances in Mechanical Engineering
author_facet Lisheng Jin
Huacai Xian
Yuying Jiang
Qingning Niu
Meijiao Xu
Dongmei Yang
author_sort Lisheng Jin
title Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
title_short Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
title_full Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
title_fullStr Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
title_full_unstemmed Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements
title_sort research on evaluation model for secondary task driving safety based on driver eye movements
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8132
publishDate 2014-01-01
description This study was designed to gain insight into the influence of performing different types of secondary task while driving on driver eye movements and to build a safety evaluation model for secondary task driving. Eighteen young drivers were selected and completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks were analyzed. Based on measures which had significant difference between the baseline and secondary tasks driving conditions, the evaluation index system was built. Method of principal component analysis (PCA) was applied to analyze evaluation indexes data in order to obtain the coefficient weights of indexes and build the safety evaluation model. Based on evaluation scores, the driving safety was grouped into five levels (very high, high, average, low, and very low) using K -means clustering algorithm. Results showed that secondary task driving severely distracts the driver and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.
url https://doi.org/10.1155/2014/624561
work_keys_str_mv AT lishengjin researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
AT huacaixian researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
AT yuyingjiang researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
AT qingningniu researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
AT meijiaoxu researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
AT dongmeiyang researchonevaluationmodelforsecondarytaskdrivingsafetybasedondrivereyemovements
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