Da Lio, M., Dona, R., Papini, G.P.R. et al. (1 more author) (2020) Agent architecture for adaptive behaviours in autonomous driving. IEEE Access, 8. pp. 154906-154923. ISSN 2169-3536
Abstract
Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Adaptive Behaviour; Affordance Competition Hypothesis; Autonomous Driving; Explainable Artificial Intelligence |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 DREAM4CARS 731593 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Jul 2020 09:40 |
Last Modified: | 24 Jan 2022 03:47 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/access.2020.3007018 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163442 |