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Modeling and Measuring Cognitive Loa...

Jahagirdar, Tanvi.

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Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars./
Author:	Jahagirdar, Tanvi.
Description:	144 p.
Notes:	Source: Masters Abstracts International, Volume: 54-04.
Contained By:	Masters Abstracts International54-04(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1587854
ISBN:	9781321722482

Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.
Jahagirdar, Tanvi.

Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars. - 144 p.

Source: Masters Abstracts International, Volume: 54-04.

Thesis (M.S.)--Arizona State University, 2015.

Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.

ISBN: 9781321722482Subjects--Topical Terms:

523869
Computer science.

Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.
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245 1 0 $a Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.
300 $a 144 p.
500 $a Source: Masters Abstracts International, Volume: 54-04.
500 $a Includes supplementary digital materials.
500 $a Adviser: Ashraf Gaffar.
502 $a Thesis (M.S.)--Arizona State University, 2015.
520 $a Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.
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