東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Inferring Takeover and Trust in SAE Level 2 Automated Vehicles.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inferring Takeover and Trust in SAE Level 2 Automated Vehicles./
Author:	Konishi, Matthew.
Description:	1 online resource (52 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Augmented reality. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30499187click for full text (PQDT)
ISBN:	9798379435318

Inferring Takeover and Trust in SAE Level 2 Automated Vehicles.
Konishi, Matthew.

Inferring Takeover and Trust in SAE Level 2 Automated Vehicles. - 1 online resource (52 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.Eng.)--Purdue University, 2023.

Includes bibliographical references

With the rise of automation in today's society, a growing amount of research is focused on improving human-automation interaction. This includes a particular emphasis on how humans trust automation, as human trust is a driving factor of human reliance on automation. Specifically, trust needs to be calibrated for successful interaction between humans and automation. To avoid trust miscalibration (i.e., over/under trust), there is a need to design human-aware systems that can predict human trust and adapt their behavior accordingly. However, current computational trust models often overlook aspects of trust that have been highlighted by qualitative modeling efforts. Specifically, it is not clear how trust develops over longer timescales (greater than minutes) or across a gap in interaction. In this thesis, a computational trust model aimed at capturing changes in trust behavior over multiple timescales and interaction gaps is developed. A Non-linear Autoregressive with Exogenous Inputs (NARX) model is chosen to predict human trust levels by leveraging behavioral, psychophysiological, and environmental data. Trust is studied in an SAE Level 2 context, using a medium-fidelity driving simulator. A unique experiment is designed such that trust dynamics can be studied across two distinct interactions, separated by a period of one week with no interaction. The data collected from this experiment are evaluated to determine which features of the data set best predicted trust. These features are then used to train multiple trust models, which are then analyzed and compared. While model analysis reveals that trust dynamics differ between interactions, it also indicates that the differences may be captured by a single model.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379435318Subjects--Topical Terms:

1620831
Augmented reality.
Index Terms--Genre/Form:

542853
Electronic books.

Inferring Takeover and Trust in SAE Level 2 Automated Vehicles.
LDR:02922nmm a2200337K 4500 001 2358445
005 20230731112653.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798379435318
035 $a (MiAaPQ)AAI30499187
035 $a (MiAaPQ)Purdue22250509
035 $a AAI30499187
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Konishi, Matthew. $3 3698975
245 1 0 $a Inferring Takeover and Trust in SAE Level 2 Automated Vehicles.
264 0 $c 2023
300 $a 1 online resource (52 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: 84-10.
500 $a Advisor: Jain, Neera.
502 $a Thesis (M.Eng.)--Purdue University, 2023.
504 $a Includes bibliographical references
520 $a With the rise of automation in today's society, a growing amount of research is focused on improving human-automation interaction. This includes a particular emphasis on how humans trust automation, as human trust is a driving factor of human reliance on automation. Specifically, trust needs to be calibrated for successful interaction between humans and automation. To avoid trust miscalibration (i.e., over/under trust), there is a need to design human-aware systems that can predict human trust and adapt their behavior accordingly. However, current computational trust models often overlook aspects of trust that have been highlighted by qualitative modeling efforts. Specifically, it is not clear how trust develops over longer timescales (greater than minutes) or across a gap in interaction. In this thesis, a computational trust model aimed at capturing changes in trust behavior over multiple timescales and interaction gaps is developed. A Non-linear Autoregressive with Exogenous Inputs (NARX) model is chosen to predict human trust levels by leveraging behavioral, psychophysiological, and environmental data. Trust is studied in an SAE Level 2 context, using a medium-fidelity driving simulator. A unique experiment is designed such that trust dynamics can be studied across two distinct interactions, separated by a period of one week with no interaction. The data collected from this experiment are evaluated to determine which features of the data set best predicted trust. These features are then used to train multiple trust models, which are then analyzed and compared. While model analysis reveals that trust dynamics differ between interactions, it also indicates that the differences may be captured by a single model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Augmented reality. $3 1620831
650 4 $a Behavior. $3 532476
650 4 $a Collaboration. $3 3556296
650 4 $a Autonomous vehicles. $3 2179092
650 4 $a Human subjects. $3 3562959
650 4 $a Computer engineering. $3 621879
650 4 $a Mechanical engineering. $3 649730
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0464
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Purdue University. $3 1017663
773 0 $t Masters Abstracts International $g 84-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30499187 $z click for full text (PQDT)