Zobrazeno 1 - 10
of 63 969
pro vyhledávání: '"Driving Behavior"'
Autor:
Garefalakis, Thodoris1 (AUTHOR) tgarefalakis@mail.ntua.gr, Michelaraki, Eva1 (AUTHOR), Roussou, Stella1 (AUTHOR), Katrakazas, Christos1 (AUTHOR), Brijs, Tom2 (AUTHOR), Yannis, George1 (AUTHOR)
Publikováno v:
EUROPEAN Transport Research Review. 11/21/2024, Vol. 16 Issue 1, p1-13. 13p.
Autor:
Zhang, D. J.1 609461317@qq.com, Shang, F. M.2
Publikováno v:
Advances in Transportation Studies. SpecialIssue2024, p15-28. 14p.
Autor:
Alfred T. Lee
In the U.S., drivers over the age of 65 now account for nearly 20% of licensed drivers. This number will increase by 25% to nearly 70 million by the year 2030. Some of these older drivers may not be capable of operating their vehicles safely in all c
Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this field can describe driving scenes and behaviors with a level of accur
Externí odkaz:
http://arxiv.org/abs/2409.20364
Autor:
Thodoris Garefalakis, Eva Michelaraki, Stella Roussou, Christos Katrakazas, Tom Brijs, George Yannis
Publikováno v:
European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024)
Abstract Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze differ
Externí odkaz:
https://doaj.org/article/2aa45d841fb940108b0fa9b2d8ae2bd8
Publikováno v:
Journal of Advanced Transportation. 4/17/2024, Vol. 2024, p1-16. 16p.
Autor:
Roussou, Stella1 (AUTHOR) s_roussou@mail.ntua.gr, Michelaraki, Eva1 (AUTHOR), Katrakazas, Christos1 (AUTHOR), Afghari, Amir Pooyan2 (AUTHOR), Al Haddad, Christelle3 (AUTHOR), Alam, Md Rakibul3 (AUTHOR), Antoniou, Constantinos3 (AUTHOR), Papadimitriou, Eleonora2 (AUTHOR), Brijs, Tom4 (AUTHOR), Yannis, George1 (AUTHOR)
Publikováno v:
EUROPEAN Transport Research Review. 7/1/2024, Vol. 16 Issue 1, p1-13. 13p.
With the development of deep learning technology, the detection and classification of distracted driving behaviour requires higher accuracy. Existing deep learning-based methods are computationally intensive and parameter redundant, limiting the effi
Externí odkaz:
http://arxiv.org/abs/2407.01864
Publikováno v:
Science Technology & Engineering. 2024, Vol. 24 Issue 15, p6493-6501. 9p.