Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Matteo Leonetti"'
Autor:
Matthew Warburton, Jack Brookes, Mohamed Hasan, Matteo Leonetti, Mehmet Dogar, He Wang, Anthony G. Cohn, Faisal Mushtaq, Mark Mon-Williams
Publikováno v:
Royal Society Open Science, Vol 11, Iss 4 (2024)
Human sensorimotor decision making has a tendency to get ‘stuck in a rut’, being biased towards selecting a previously implemented action structure (hysteresis). Existing explanations propose this is the consequence of an agent efficiently modify
Externí odkaz:
https://doaj.org/article/5688c05aa7914ea6af73d114a36b6037
Autor:
Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Jac Billington, Gustav Markkula
Publikováno v:
IEEE Access, Vol 10, Pp 118888-118899 (2022)
Many models account for the traffic flow of road users, but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on an existing model of human sensorimotor control, we
Externí odkaz:
https://doaj.org/article/e254949297504dd9938035d8bedf0332
Autor:
Rachael K Raw, Richard M Wilkie, Richard J Allen, Matthew Warburton, Matteo Leonetti, Justin H G Williams, Mark Mon-Williams
Publikováno v:
PLoS ONE, Vol 14, Iss 2, p e0211706 (2019)
Some activities can be meaningfully dichotomised as 'cognitive' or 'sensorimotor' in nature-but many cannot. This has radical implications for understanding activity limitation in disability. For example, older adults take longer to learn the serial
Externí odkaz:
https://doaj.org/article/369cbf1801fd44a2ae1f28ac0efc479c
Autor:
Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
Publikováno v:
Srinivasan, A R, Lin, Y-S, Antonello, M, Knittel, A, Hasan, M, Hawasly, M, Redford, J, Ramamoorthy, S, Leonetti, M, Billington, J, Romano, R & Markkula, G 2023, ' Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior? ', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 7166-7177 . https://doi.org/10.1109/TITS.2023.3263358
Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focus on quantitative error metrics like the root mean square error (RMSE)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::494f6f79905cff46b9d597bef3e40565
https://doi.org/10.1109/tits.2023.3263358
https://doi.org/10.1109/tits.2023.3263358
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264115
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b50386f91998cb48196137bea51e9df5
https://doi.org/10.1007/978-3-031-26412-2_5
https://doi.org/10.1007/978-3-031-26412-2_5
Autor:
Lennart Wachowiak, Peter Tisnikar, Gerard Canal, Andrew Coles, Matteo Leonetti, Oya Celiktutan
Publikováno v:
2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1004be4fbf0c56b27e2e1508b6938e5f
https://doi.org/10.1109/ro-man53752.2022.9900589
https://doi.org/10.1109/ro-man53752.2022.9900589
Autor:
Gustav Markkula, Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Jac Billington, Matteo Leonetti, Amir Hossein Kalantari, Yue Yang, Yee Mun Lee, Ruth Madigan, Natasha Merat
Interaction between road users is a societally important type of human interaction, which has been hypothesised to draw on a variety of cognitive mechanisms. These mechanisms are mostly studied and modelled in separate subfields of psychology, despit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32e8b3ea65b6511ae70a36da28cd1836
https://doi.org/10.31234/osf.io/hdxbs
https://doi.org/10.31234/osf.io/hdxbs
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling. 31:551-559
Heuristic planning has a central role in classical planning applications and competitions. Thanks to this success, there has been an increasing interest in using Deep Learning to create high-quality heuristics in a supervised fashion, learning from o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1da87df790abd25545a4a46cb91c402d
https://doi.org/10.1609/icaps.v31i1.16003
https://doi.org/10.1609/icaps.v31i1.16003
Autor:
Richard Romano, Gustav Markkula, Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Mohamed Hasan, Jac Billington
Publikováno v:
ITSC
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error metrics, for example average distance between model-generated and observ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d77db0ba36a17a62ed6fb481b2f9825
https://doi.org/10.1109/itsc48978.2021.9564791
https://doi.org/10.1109/itsc48978.2021.9564791
Autor:
Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Jac Billington, Gustav Markkula
Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on the concept of sensorimotor control, we develop a mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4712645deaa73942b9c1489ee14e0122