東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Generative AI for Critical Digital T...

Ding, Wenhao.

FindBook

Google Book

Amazon

博客來

Generative AI for Critical Digital Twins.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Generative AI for Critical Digital Twins./
作者:	Ding, Wenhao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	247 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Robotics. -
電子資源:	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.
LDR:03615nmm a2200385 4500 001 2403000
005 20241104055838.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382611853
035 $a (MiAaPQ)AAI31242974
035 $a AAI31242974
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ding, Wenhao. $0 (orcid)0000-0003-3218-8792 $3 3773263
245 1 0 $a Generative AI for Critical Digital Twins.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 247 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
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.
590 $a School code: 0041.
650 4 $a Robotics. $3 519753
650 4 $a Computer science. $3 523869
650 4 $a Automotive engineering. $3 2181195
650 4 $a Mechanical engineering. $3 649730
653 $a Autonomous driving
653 $a Digital twins
653 $a Autonomous robots
653 $a Accuracy
690 $a 0771
690 $a 0984
690 $a 0540
690 $a 0548
710 2 $a Carnegie Mellon University. $b Mechanical Engineering. $3 2096240
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0041
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31242974