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pro vyhledávání: '"Atakishiyev, Shahin"'
Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundation for real-time actions, self-driving behavior primarily remains opa
Externí odkaz:
http://arxiv.org/abs/2404.07383
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integra
Externí odkaz:
http://arxiv.org/abs/2403.12176
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms. However, as autonomous driving technology is a safety-critical
Externí odkaz:
http://arxiv.org/abs/2307.10408
Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly trans
Externí odkaz:
http://arxiv.org/abs/2112.11561
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinfo
Externí odkaz:
http://arxiv.org/abs/2111.10518
In data dominated systems and applications, a concept of representing words in a numerical format has gained a lot of attention. There are a few approaches used to generate such a representation. An interesting issue that should be considered is the
Externí odkaz:
http://arxiv.org/abs/1907.07672
Akademický článek
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Autor:
Mi-Young Kim, Atakishiyev, Shahin, Bashier Babiker, Housam Khalifa, Farruque, Nawshad, Goebel, Randy, Zaïane, Osmar R., Motallebi, Mohammad-Hossein, Rabelo, Juliano, Syed, Talat, Hengshuai Yao, Chun, Peter
Publikováno v:
Machine Learning & Knowledge Extraction; Dec2021, Vol. 3 Issue 4, p900-921, 22p
