Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Liviu A. Marina"'
Autor:
Liviu A. Marina
Publikováno v:
International Journal of Robotic Computing. 2
Numerous self-driving cars algorithms rely on grid maps for motion planning, obstacles avoidance, or environment perception. Obtained from fused sensory information, the occupancy grids (OGs) are nowadays among the most popular solutions used in seri
Autor:
Liviu Octavian Marina Fiț
Publikováno v:
Altarul Reîntregirii. :137-158
Autor:
Liviu Octavian Marina Fiț
Publikováno v:
Altarul Reîntregirii. :187-211
Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL) systems. Current deep learning solutions for autonomous driving are su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d765fe3e973c86dec56cfabb5e33439
http://arxiv.org/abs/1906.10971
http://arxiv.org/abs/1906.10971
Autor:
Sorin Mihai Grigorescu, Andrei Vasilcoi, Bogdan Trasnea, Tiberiu T. Cocias, Liviu A. Marina, Florin Moldoveanu
Publikováno v:
IRC
Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed for understanding
Publikováno v:
2018 22nd International Conference on System Theory, Control and Computing (ICSTCC).
It is becoming increasingly evident that data science and artificial intelligence are key technologies in the future of automotive industry. Artificial Intelligence (AI) successes are significant, with some limitations in terms of algorithms portabil
Publikováno v:
2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP).
In this paper, we introduce a real-time approach for object recognition, applying the Faster Region-based Convolutional Network (Faster-RCNN) concept in an affordable and open source simulator that can be used to train and test Deep Neural Networks (
Publikováno v:
Proceedings of the International Multidisciplinary Scientific GeoConference SGEM. 2019, Vol. 19 Issue 1, p671-678. 8p.
Autor:
Serôdio, Maria Helena
Publikováno v:
Sinais de Cena. dez2013, Issue 20, p87-91. 5p.