Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Gotlieb, Arnaud"'
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques.
Externí odkaz:
http://arxiv.org/abs/2409.10203
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
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The me
Externí odkaz:
http://arxiv.org/abs/2403.18731
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
Reinforcement Learning is the premier technique to approach sequential decision problems, including complex tasks such as driving cars and landing spacecraft. Among the software validation and verification practices, testing for functional fault dete
Externí odkaz:
http://arxiv.org/abs/2403.15065
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
Autor:
Bernabé, Pierre, Gotlieb, Arnaud, Legeard, Bruno, Marijan, Dusica, Sem-Jacobsen, Frank Olaf, Spieker, Helge
In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) me
Externí odkaz:
http://arxiv.org/abs/2310.15586
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
Automated test execution scheduling is crucial in modern software development environments, where components are frequently updated with changes that impact their integration with hardware systems. Building test schedules, which focus on the right te
Externí odkaz:
http://arxiv.org/abs/2306.01529
Autor:
Ziat, Ghiles, Botbol, Vincent, Dien, Matthieu, Gotlieb, Arnaud, Pépin, Martin, Dubois, Catherine
In program verification, constraint-based random testing is a powerful technique which aims at generating random test cases that satisfy functional properties of a program. However, on recursive constrained data-structures (e.g., sorted lists, binary
Externí odkaz:
http://arxiv.org/abs/2208.12747