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Evaluation of Trust in Autonomous Sy...
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Gebru, Biniam Tadesse.
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Evaluation of Trust in Autonomous Systems: Human Trust Sensing and Trustworthy Autonomous Driving.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Evaluation of Trust in Autonomous Systems: Human Trust Sensing and Trustworthy Autonomous Driving./
作者:
Gebru, Biniam Tadesse.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:
129 p.
附註:
Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:
Dissertations Abstracts International84-12B.
標題:
Electrical engineering. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30314195
ISBN:
9798379701376
Evaluation of Trust in Autonomous Systems: Human Trust Sensing and Trustworthy Autonomous Driving.
Gebru, Biniam Tadesse.
Evaluation of Trust in Autonomous Systems: Human Trust Sensing and Trustworthy Autonomous Driving.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 129 p.
Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Thesis (Ph.D.)--North Carolina Agricultural and Technical State University, 2023.
This item must not be sold to any third party vendors.
As complex autonomous systems become increasingly ubiquitous, from self-driving vehicles to decision support systems, their deployment and integration into everyday use become a significant endeavor. While automation and autonomy have progressed significantly, human oversight and involvement remain crucial in almost all application areas, such as transportation, healthcare, and emergency response. Human-machine trust relationship is one of the primary aspects that characterize a successful integration. In human-autonomous system interaction, proper use of machines and autonomous systems depends on both the human and machine counterparts. On the one hand, it depends on how properly the human relies on the autonomous system regarding a situation or task based on willingness and experience. On the other hand, it depends on how well the machine carries out the mission and how well it conveys essential information on the way the job is done. It, therefore, becomes vital to understand the factors dictating this relationship from both ends of the interaction and to have a real-time sensing mechanism of trust markers for effective adaptability. This dissertation presents: (i) a comprehensive systematic investigation into the characterization and evaluation of trust from both human-centric and machine-centric perspectives; (ii) extraction and explainability evaluation of features that influence this latent and multi-dimensional phenomenon, trust, based on data collected using real-time sensing of psychophysiological responses; and (iii) in addition, this dissertation evaluates the safe driving behaviors, and by extension the trustworthiness, of an autonomous vehicle that performs beyond adhering to the road and traffic rules taking into account other road users' uncertain and risky behaviors as well as the autonomous vehicle's capabilities using the responsibility sensitivity safety framework.
ISBN: 9798379701376Subjects--Topical Terms:
649834
Electrical engineering.
Subjects--Index Terms:
Human-machine interaction
Evaluation of Trust in Autonomous Systems: Human Trust Sensing and Trustworthy Autonomous Driving.
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As complex autonomous systems become increasingly ubiquitous, from self-driving vehicles to decision support systems, their deployment and integration into everyday use become a significant endeavor. While automation and autonomy have progressed significantly, human oversight and involvement remain crucial in almost all application areas, such as transportation, healthcare, and emergency response. Human-machine trust relationship is one of the primary aspects that characterize a successful integration. In human-autonomous system interaction, proper use of machines and autonomous systems depends on both the human and machine counterparts. On the one hand, it depends on how properly the human relies on the autonomous system regarding a situation or task based on willingness and experience. On the other hand, it depends on how well the machine carries out the mission and how well it conveys essential information on the way the job is done. It, therefore, becomes vital to understand the factors dictating this relationship from both ends of the interaction and to have a real-time sensing mechanism of trust markers for effective adaptability. This dissertation presents: (i) a comprehensive systematic investigation into the characterization and evaluation of trust from both human-centric and machine-centric perspectives; (ii) extraction and explainability evaluation of features that influence this latent and multi-dimensional phenomenon, trust, based on data collected using real-time sensing of psychophysiological responses; and (iii) in addition, this dissertation evaluates the safe driving behaviors, and by extension the trustworthiness, of an autonomous vehicle that performs beyond adhering to the road and traffic rules taking into account other road users' uncertain and risky behaviors as well as the autonomous vehicle's capabilities using the responsibility sensitivity safety framework.
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