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Generative AI for Critical Digital T...

Ding, Wenhao.

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Generative AI for Critical Digital Twins.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generative AI for Critical Digital Twins./
Author:	Ding, Wenhao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	247 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31242974
ISBN:	9798382611853

Generative AI for Critical Digital Twins.
Ding, Wenhao.

Generative AI for Critical Digital Twins. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 247 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2024.

Training and evaluating autonomous robots within the real world present significant challenges and risks, emanating from unpredictable environments, safety concerns, ethical dilemmas, and limited human oversight. As a mitigation strategy, the use of realistic simulations, also known as digital twins, offers virtual duplication of the actual system or environment, thus fostering the development of trustworthy autonomy.Digital twins enable developers to evaluate the performance of systems in various scenarios to identify potential risks or failure cases. It facilitates the accumulation and subsequent analysis of datasets, which serve to validate and calibrate the autonomous system's perception and decision-making algorithms. By comparing the behavior of the digital twin with real-world data, developers can identify discrepancies, improve accuracy, and enhance the system's safety and reliability.Scenarios that embody dynamic and interactive components reflect the intricacies of digital twins and take precedence in significance. One main value of digital twins is helping us understand how objects interact and behave. For example, in autonomous driving, the behavior of vehicles, pedestrians, and traffic conditions are crucial components of scenarios that need to be accurately modeled. However, not all scenarios in digital twins are created equal. In the pursuit of developing trustworthy autonomy, ordinary scenarios often prove insufficient in subjecting autonomous systems to extreme conditions where safety and robustness are paramount. Although critical scenarios hold the potential to expose model vulnerabilities, their rare occurrence creates a challenge. The process of manually identifying or extrapolating such critical scenarios from normal data or expert design proves not only inefficient but also contains substantial human biases.My doctoral research seeks to harness the potential of generative AI to explore two pivotal questions: (1) Which scenarios are critical in existing data and (2) How to generate such scenarios in digital twins? The proposal begins with the definition of critical scenarios and the corresponding optimization problem and subsequently delves into three distinct categories of scenario generation frameworks: data-driven generative models, adversarial generative models, and knowledge-guided generative models. Concluding this thesis is future directions that effectively combine generation resources from different perspectives and improve the data flywheel by.

ISBN: 9798382611853Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Autonomous driving

Generative AI for Critical Digital Twins.
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500 $a Advisor: Zhao, Ding.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2024.
520 $a Training and evaluating autonomous robots within the real world present significant challenges and risks, emanating from unpredictable environments, safety concerns, ethical dilemmas, and limited human oversight. As a mitigation strategy, the use of realistic simulations, also known as digital twins, offers virtual duplication of the actual system or environment, thus fostering the development of trustworthy autonomy.Digital twins enable developers to evaluate the performance of systems in various scenarios to identify potential risks or failure cases. It facilitates the accumulation and subsequent analysis of datasets, which serve to validate and calibrate the autonomous system's perception and decision-making algorithms. By comparing the behavior of the digital twin with real-world data, developers can identify discrepancies, improve accuracy, and enhance the system's safety and reliability.Scenarios that embody dynamic and interactive components reflect the intricacies of digital twins and take precedence in significance. One main value of digital twins is helping us understand how objects interact and behave. For example, in autonomous driving, the behavior of vehicles, pedestrians, and traffic conditions are crucial components of scenarios that need to be accurately modeled. However, not all scenarios in digital twins are created equal. In the pursuit of developing trustworthy autonomy, ordinary scenarios often prove insufficient in subjecting autonomous systems to extreme conditions where safety and robustness are paramount. Although critical scenarios hold the potential to expose model vulnerabilities, their rare occurrence creates a challenge. The process of manually identifying or extrapolating such critical scenarios from normal data or expert design proves not only inefficient but also contains substantial human biases.My doctoral research seeks to harness the potential of generative AI to explore two pivotal questions: (1) Which scenarios are critical in existing data and (2) How to generate such scenarios in digital twins? The proposal begins with the definition of critical scenarios and the corresponding optimization problem and subsequently delves into three distinct categories of scenario generation frameworks: data-driven generative models, adversarial generative models, and knowledge-guided generative models. Concluding this thesis is future directions that effectively combine generation resources from different perspectives and improve the data flywheel by.
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650 4 $a Automotive engineering. $3 2181195
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653 $a Autonomous driving
653 $a Digital twins
653 $a Autonomous robots
653 $a Accuracy
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690 $a 0984
690 $a 0540
690 $a 0548
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