Vitus Alexander Lüntzel, M. Sc. KIT

Vitus Alexander Lüntzel, M. Sc.

  • Engesserstr. 5

    76131 Karlsruhe

M. Sc. Vitus Lüntzel

Curriculum Vitae

  • Research associate at ITIV since February 2022
  • Master studies in electrical engineering and information technology with specialization in signal processing at KIT
    • Graduation in December 2021
    • Master thesis: "Simulation von realitätsnahen Batteriespannungsverläufen an einem Hardware-in-the-Loop Prüfstand".
  • Bachelor's degree in electrical engineering and information technology with a specialization in electronic systems at the BHT (dual)
    • Graduation in September 2018
    • In partnership with Siemens AG
  • Training as industrial technologist at the Siemens Academy of Technology
    • Graduation in September 2016


Teaching

  • Internship Information Technology I

Research


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Development processes for safe systems

This is a forward-looking direction, especially in the automotive sector with over-the-air updates. Safe systems are an integral part of our everyday lives. In order to bring them to market, certifications are required. Due to the rise of cyber-physical systems, with high software capabilities and strong connectivity, certification is a challenge: every change, especially to safety-critical functions, requires a complex reanalysis. In our research, we are developing new methods for incremental safety analyses that identify and re-analyze the system parts affected by the change.

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Model-based vehicle development

Mobility of the future is driven by model-based vehicle development. Models enable better documentation, greater reusability, and automated consistency checks - essential for modern, networked vehicle systems. A model can be a circuit diagram, but also the code or the CAD drawing. Continuous development of real systems takes place on our in-house research platform. This enables us to test and validate new debelopment methods.

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Cooperation with Karlsruhe Zoo
Since December 2021, the Karlsruhe Zoological Garden and ITIV have offering joint theses. The focus is on two central topics: behavioral monitoring and biodiversity monitoring. Within behavioral monitoring, machine learning methods are primarily applied to video data in order to gain knowledge about habits and health development of the animals. Biodiversity monitoring is based on species and individual recognition using sensor networks.

 

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