Mario Berk, M.Sc.

Room: N3624

Phone: +49 89 289 23295


Office hours: by arrangement

Curriculum Vitae

  • Since 2015 PhD student at the Engineering Risk Analysis Group, Technische Universität München in cooperation with AUDI AG, INI.TUM
  • 2013 – 2015 M.Sc. in Civil Engineering, Technische Universität München
  • 2009 – 2012 B.Sc. in Environmental Engineering, Technische Universität München


  • Topic:
    • Proofing the reliability of environmental sensors with the example of optical time-of-flight sensors
  • Background:
    • Environmental perception with multiple sensors enables advanced driver assistance systems (ADAS) and different levels of functional safe driving automation
    • Depending on the level of automation, failure of the full sensor set could lead to adverse consequences
  • Tasks:
    • Developing a probabilistic framework that allows to assess the reliability of optical time-of-flight sensors in the context of autonomous vehicles
    • Taking into account possible adverse effects on the sensors (e.g. weather, radiation, spoofing)


  • S17: Teaching assistant for the lecture Reliability of Engineering Systems
  • S16: Teaching assistant for the lecture “Risk Analysis”
  • W16/17: Teaching assistant for the lecture “Risk Assessment”

Supervision of student projects

  • Simon Mrowietz, 2017 - Design Flood Estimation in Bavarian Alpine (and sub-Alpine) Catchments


  • Berk M., Kroll H.-M., Schubert O., Buschardt B., Straub D. (2017). Bayesian self-referencing reliability assessment of sensor systems: Theory and application to automotive environment sensing. European Safety and Reliability Conference, ESREL, Jun 18-22 2017, Portorož.
  • Berk, M., Kroll, H., Schubert, O., Buschardt, B. et al. (2017): Bayesian Test Design for Reliability Assessments of Safety-Relevant Environment Sensors Considering Dependent Failures, SAE Technical Paper 2017-01-0050, 2017, doi:10.4271/2017-01-0050.
  • Berk M., Kroll H.-M., Schubert O., Buschardt B., Straub D. (2016): Zuverlässigkeitsanalyse umfelderfassender Sensorik. Eine stochastische Methodik zur Berücksichtigung von Umgebungseinflüssen am Beispiel von LiDAR Sensoren. 32. VDI/VW Gemeinschaftstagung Fahrerassistenz und automatisiertes Fahren. VDI Berichte, Bd. 2288. Düsseldorf: VDI Verlag GmbH 2016, S. 455–475
  • Berk M., Špačková O., Straub D. (2016): Design flood estimation in ungauged basins: probabilistic extension of the design-storm concept. Geophysical Research Abstracts, Vol. 18, EGU2016-1520, EGU General Assembly 2016, Vienna