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Towards Practical Driver Cognitive L...

Liu, Cheng Chen.

Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information./
Author:	Liu, Cheng Chen.
Description:	1 online resource (126 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355450552

Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information.
Liu, Cheng Chen.

Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information. - 1 online resource (126 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.A.S.)--University of Toronto (Canada), 2017.

Includes bibliographical references

With growing popularities of intelligent in-vehicle technologies, monitoring of driver cognitive load is becoming more important for both safety and comfort concerns. This task is recognized to be challenging due to limited prior knowledge. This thesis explores the feasibility of classifying driver cognitive load levels based on visual attention features.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355450552Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information.
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020 $a 9780355450552
035 $a (MiAaPQ)AAI10637119
035 $a (MiAaPQ)toronto:16694
035 $a AAI10637119
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Liu, Cheng Chen. $3 1148627
245 1 0 $a Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information.
264 0 $c 2017
300 $a 1 online resource (126 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Konstantinos N. Plataniotis.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2017.
504 $a Includes bibliographical references
520 $a With growing popularities of intelligent in-vehicle technologies, monitoring of driver cognitive load is becoming more important for both safety and comfort concerns. This task is recognized to be challenging due to limited prior knowledge. This thesis explores the feasibility of classifying driver cognitive load levels based on visual attention features.
520 $a First, we contribute a dataset collected from 37 experienced drivers. The collection process focuses on eliciting three levels of cognitive load effectively. The resulting dataset consists a total of eight measurements, gathered from visual, vehicular, physiological sensors and subjective-questionnaires.
520 $a Next, we focus on the eye-tracking modality and propose meta-features for capturing variations in visual attention intensity and direction. Then, five machine learning algorithms are applied for subject-independent classification. Issues arose from the machine learning workflow, such as evaluation bias, are examined. The most promising algorithm (Random Forest) achieves 70.3% accuracy on classifying between high and low cognitive load.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Artificial intelligence. $3 559380
650 4 $a Automotive engineering. $3 1104081
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0800
690 $a 0540
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Toronto (Canada). $b Electrical and Computer Engineering. $3 1148628
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10637119 $z click for full text (PQDT)

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