38 citations · 56 across the 20 of their papers we have counts for
6 papers · 1 filter
Efficient Milling Quality Prediction with Explainable Machine Learning
Dennis Gross, Helge Spieker, Arnaud Gotlieb +2
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 empl…
Evaluating Human Trajectory Prediction with Metamorphic Testing
Helge Spieker, Nassim Belmecheri, Arnaud Gotlieb +1
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 predictio…
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
Dennis Gross, Helge Spieker, Arnaud Gotlieb +1
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,…
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Exp…
Testing for Fault Diversity in Reinforcement Learning
Quentin Mazouni, Helge Spieker, Arnaud Gotlieb +1
Reinforcement Learning is the premier technique to approach sequential decision problems, including complex tasks such as driving cars and landing spacecraft. Among the software va…
Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables the interpretation of a…