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pro vyhledávání: '"Belmecheri, Nassim"'
The prediction of human trajectories is important for planning in autonomous systems that act in the real world, e.g. automated driving or mobile robots. Human trajectory prediction is a noisy process, and no prediction does precisely match any futur
Externí odkaz:
http://arxiv.org/abs/2407.18756
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative repr
Externí odkaz:
http://arxiv.org/abs/2403.16908
We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables the interpretation of an automated vehicle's environment using sensor data and machine learni
Externí odkaz:
http://arxiv.org/abs/2403.09668
The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the p
Externí odkaz:
http://arxiv.org/abs/2308.12755
Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the runnin
Externí odkaz:
http://arxiv.org/abs/2203.02696
Autor:
Belmecheri, Nassim1,2 (AUTHOR) aribi.noureddine@gmail.com, Aribi, Noureddine1 (AUTHOR) ylebbah@gmail.com, Lazaar, Nadjib3 (AUTHOR) nadjib.Lazaar@lirmm.fr, Lebbah, Yahia1 (AUTHOR), Loudni, Samir4 (AUTHOR) samir.loudni@imt-atlantique.fr
Publikováno v:
Algorithms. May2023, Vol. 16 Issue 5, p218. 26p.
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