Patrick Petersen
Center for Information Technology
10 Papers
14 Citations
Patrick Petersen is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Engineering & Computer science. The author has an hindex of 1, co-authored 4 publications. Previous affiliations of Patrick Petersen include Karlsruhe Institute of Technology & Forschungszentrum Informatik.
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Papers
Towards a Data Engineering Process in Data-Driven Systems Engineering
Patrick Petersen,Hanno Stage,Jacob Langner,Lennart Ries,Philipp Rigoll,Carl Philipp Hohl,Eric Sax +6 more
- 24 Oct 2022
TL;DR: In this article , the authors proposed a data engineering process in data-driven automotive systems engineering (ASE), which aims to take a step towards the introduction of a Data Engineering process in Data-Driven Automotive Systems Engineering.
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Validation of range estimation for electric vehicles based on recorded real-world driving data
Patrick Petersen,Jacob Langner,Stefan Otten,Eric Sax,Stefan Scheubner,Moritz Vaillant,Sebastian Funfgeld,F. Porsche +7 more
- 01 Jan 2019
TL;DR: In this article, the authors proposed a range estimation method for battery electric vehicles, which can reduce the range anxiety of battery electric vehicle drivers and reduce greenhouse gas emissions, but short maximum range and missing charging infrastructure limits user acceptance.
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Training and Validation Methodology for Range Estimation Algorithms
Patrick Petersen,Adam Thor Thorgeirsson,Adam Thor Thorgeirsson,Stefan Scheubner,Stefan Otten,Frank Gauterin,Eric Sax +6 more
- 12 Oct 2019
TL;DR: This paper proposes a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches that considers the evaluation of driver-specific and driver-unspecific performance.
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A Data-driven Energy Estimation based on the Mixture of Experts Method for Battery Electric Vehicles
Patrick Petersen,Thomas Rudolf,Eric Sax +2 more
- 01 Jan 2022
TL;DR: In this article , the authors proposed a data-driven approach for the energy estimation of BEVs by utilizing ensemble learning to achieve a feature-specific estimation, which improved the overall estimation by combining models via the mixture of experts method compared to a monolithic trained neural network.
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Evaluating User Interfaces Supporting Change Detection in Aerial Images and Aerial Image Sequences
Jutta Hild,Günter Saur,Patrick Petersen,Michael Voit,Elisabeth Peinsipp-Byma,Jürgen Beyerer +5 more
- 15 Jul 2018
TL;DR: The benefit of optimized image presentation and the availability of a change mask computed by an automated change detection algorithm is evaluated and shows better change detection performance using the alternating flicker image presentation for the large majority of data sets.
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