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STPA for learning-enabled systems : a survey and a new practice
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Qi, Yi, Dong, Yi, Khastgir, Siddartha, Jennings, Paul. A., Zhao, Xingyu and Huang, Xiaowei (2023) STPA for learning-enabled systems : a survey and a new practice. In: 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023, Bilbao, Bizkaia, Spain, 24-28 Sep 2023 (In Press)
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WRAP-STPA-learning-enabled-systems-a-survey-a-new-practice-23.pdf - Accepted Version - Requires a PDF viewer. Download (1255Kb) | Preview |
Abstract
Systems Theoretic Process Analysis (STPA) is a systematic approach for hazard analysis that has been used across many industrial sectors including transportation, energy, and defense. The unstoppable trend of using Machine Learning (ML) in safety-critical systems has led to the pressing need of extending STPA to Learning-Enabled Systems (LESs). Al- though works have been carried out on various example LESs, without a systematic review, it is unclear how effective and generalisable the extended STPA methods are, and whether further improvements can be made. To this end, we present a systematic survey of 31 papers, summarising them from five perspectives (attributes of concern, objects under study, modifications, derivatives and processes being modelled). Furthermore, we identify room for improvement and accordingly introduce DeepSTPA, which enhances STPA from two aspects that are missing from the state-of-the-practice: (i) Control loop structures are explicitly extended to identify hazards from the data-driven development process spanning the ML lifecycle; (ii) Fine-grained functionalities are modelled at the layer-wise levels of ML models to detect root causes. We demonstrate and compare DeepSTPA and STPA through a case study on an autonomous emergency braking system.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Library of Congress Subject Headings (LCSH): | System safety, Industrial safety -- Data processing, Machine learning , Automated vehicles -- Brakes -- Safety measures, Artificial intelligence | ||||||
Publisher: | IEEE | ||||||
Official Date: | 2023 | ||||||
Dates: |
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Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Reuse Statement (publisher, data, author rights): | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 14 July 2023 | ||||||
Date of first compliant Open Access: | 17 July 2023 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023 | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Bilbao, Bizkaia, Spain | ||||||
Date(s) of Event: | 24-28 Sep 2023 | ||||||
Related URLs: | |||||||
Open Access Version: |
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