Calinescu, Radu orcid.org/0000-0002-2678-9260, Imrie, Calum Corrie, Mangal, Ravi et al. (4 more authors) (2024) Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components. IEEE Transactions on Software Engineering. pp. 1374-1395. ISSN 0098-5589
Abstract
We present DeepDECS, a new method for the synthesis of correct-by-construction software controllers for autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making processes. Despite major advances in deep learning in recent years, providing safety guarantees for these systems remains very challenging. Our controller synthesis method addresses this challenge by integrating DNN verification with the synthesis of verified Markov models. The synthesised models correspond to discrete-event software controllers guaranteed to satisfy the safety, dependability and performance requirements of the autonomous system, and to be Pareto optimal with respect to a set of optimisation objectives. We evaluate the method in simulation by using it to synthesise controllers for mobile-robot collision limitation, and for maintaining driver attentiveness in shared-control autonomous driving.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors |
Keywords: | discrete-event controller synthesis,Markov model,deep neural network,uncertainty quantification,neuro-symbolic AI |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EPSRC EP/V026747/1 |
Depositing User: | Pure (York) |
Date Deposited: | 04 Apr 2024 08:20 |
Last Modified: | 21 Jan 2025 18:12 |
Published Version: | https://doi.org/10.1109/TSE.2024.3385378 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TSE.2024.3385378 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211131 |
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Description: Controller Synthesis for Autonomous Systems With Deep-Learning Perception Components
Licence: CC-BY-NC-ND 2.5