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Safety-Aware Autonomous Systems: Preparing Robots for Life in the Real World
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8546-4431
2022 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Real‐world autonomous systems are expected to be increasingly deployed and operating in real‐world environments over the coming decades. Autonomous systems such as AI‐enabled robotic systems and intelligent transportation systems, will alleviate mundane human work, provide new services, and facilitate a smarter and more flexible infrastructure. The real‐ world environments affected include workplaces, public spaces, and homes. 

To ensure safe operations, in for example the vicinity of people, it is paramount that the autonomous systems are explainable, behave predictable, and can handle that the real world is ever changing and only partially observable. 

To deal with a dynamic and changing environment, consistently and safely, it is necessary to have sound uncertainty management. Explicit uncertainty quantification is fundamental to providing probabilistic safety guarantees that can also be monitored during runtime to ensure safety in new situations. It is further necessary for well‐grounded prediction and classification uncertainty, for achieving task effectiveness with high robustness and for dealing with unknown unknowns, such as world model divergence, using anomaly detection. 

This dissertation focuses on the notion of motion in terms of trajectories, from recognizing – to anticipating – to generating – to monitoring that it fulfills expectations such as predictability or other safety constraints during runtime. Efficiency, effectiveness, and safety are competing qualities, and in safety-critical applications the required degree of safety makes it very challenging to reach useful levels of efficiency and effectiveness. To this end, a holistic perspective on agent motion in complex and dynamic environments is investigated. This work leverage synergies in well‐founded formalized interactions and integration between learning, reasoning, and interaction, and demonstrate jointly efficient, effective, and safe capabilities for autonomous systems in safety‐critical situations. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. , p. 244
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2262
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-189983DOI: 10.3384/9789179295028ISBN: 9789179295011 (print)ISBN: 9789179295028 (electronic)OAI: oai:DiVA.org:liu-189983DiVA, id: diva2:1710871
Public defence
2022-12-09, Ada Lovelace, B-huset, Campus Valla, Linköping, 13:15
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Available from: 2022-11-15 Created: 2022-11-15 Last updated: 2022-11-16Bibliographically approved

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