東華大學圖書館 |

Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data // Shubham Agrawal.
Author:	Agrawal, Shubham,
Description:	1 electronic resource (149 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Telematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30504328
ISBN:	9798379828561

Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data /
Agrawal, Shubham,

Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data /Shubham Agrawal. - 1 electronic resource (149 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers' interactions with these technologies using physiological data collected in two sets of driving simulator experiments.This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers' route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver's cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision.Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver's pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver's pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance.Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver's situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers' route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.

English

ISBN: 9798379828561Subjects--Topical Terms:

534653
Telematics.

Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data /
LDR:05153nmm a22004093i 4500 001 2432048
005 20250626110551.5
006 m o d
007 cr|nu||||||||
008 261011s2020 miu||||||m |||||||eng d
020 $a 9798379828561
035 $a (MiAaPQD)AAI30504328
035 $a (MiAaPQD)Purdue13362923
035 $a AAI30504328
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Agrawal, Shubham, $e author. $3 3821134
245 1 0 $a Understanding the Cognitive and Psychological Impacts of Emerging Technologies on Driver Decision-Making Using Physiological Data / $c Shubham Agrawal.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 1 electronic resource (149 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: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisors: Labi, Samuel Committee members: Peeta, Srinivas; Gkritza, Konstantina Nadia; Parsons, Paul.
502 $b Ph.D. $c Purdue University $d 2020.
520 $a Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers' interactions with these technologies using physiological data collected in two sets of driving simulator experiments.This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers' route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver's cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision.Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver's pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver's pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance.Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver's situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers' route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.
546 $a English
590 $a School code: 0183
650 4 $a Telematics. $3 534653
650 4 $a Electroencephalography. $3 543293
650 4 $a Decision making. $3 517204
650 4 $a Cognition & reasoning. $3 3556293
650 4 $a Electrocardiography. $3 773588
650 4 $a Cognitive psychology. $3 523881
650 4 $a Information technology. $3 532993
650 4 $a Neurosciences. $3 588700
650 4 $a Psychology. $3 519075
650 4 $a Statistics. $3 517247
690 $a 0633
690 $a 0489
690 $a 0317
690 $a 0621
690 $a 0463
710 2 $a Purdue University. $e degree granting institution. $3 3759105
720 1 $a Labi, Samuel $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 0183
791 $a Ph.D.
792 $a 2020
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30504328