Mario Berk, M.Sc.

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Curriculum Vitae

  • 2015-2018 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:
    • Developing statistical methods to assess and demonstrate the reliability of the automotive environment perception in the context of automated driving vehicles.
  • Background:
    • The environment perception provided by lidar, radar and camera sensors enables advanced driver assistance systems (ADAS) and different levels of functional safe driving automation.
    • Depending on the level of driving automation, failure of the full sensor set could lead to adverse consequences.
    • Due to the large required test effort, a random environment and the difficulty of generating a reference truth in complex driving situations, the safety validation of the sensors is challenging.
  • Tasks:
    • Definition of reliability metrics for the environment perception.
    • Developing learning methods that allow to quantify the effect of possible adverse influences on the sensors (e.g. weather, radiation, spoofing).
    • Developing statistical methods to estimate the effect of environmental conditions on the sensors by means of simulation.
    • Studying which aspects of the sensor reliability can be learned without reference truth by exploiting sensor redundancy.


  • S18: Teaching assistant for the lecture “Umweltmonitoring und Risikomanagement”.
  • 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

  • Tobias Zeh 2018 - Optimizing quality assurance measures for flexible manufacturing scenarios via decision analysis
  • Zayd Hamdi 2018 - Storm damage assessment in forests using deep learning and remote sensing
  • Michael Dura, 2017 - Modeling the effect of precipitation on automotive LIDAR detection capability
  • Simon Mrowietz, 2017 - Design flood estimation in Bavarian alpine (and sub-alpine) catchments


  • M. Berk, O. Schubert, H.-M. Kroll, B. Buschardt, and D. Straub, “Absicherung der Umfeldwahrnehmung von hoch- und vollautomatisierten Fahrzeugen,” in Fahrerassistenzsysteme und automatisiertes Fahren: 34. VDI/VW-Gemeinschaftstagung, Wolfsburg, 2018, pp. 165–184.
  • M. Berk et al., “A Stochastic Physical Simulation Framework to Quantify the Effect of Rainfall on Automotive Lidar,” in SAE Technical Paper 2019-01-0134, 2019.
  • Berk M., Špačková O., Straub D. (2017). Probabilistic Design Storm Method for Improved Flood Estimation in Ungauged Catchments. Water Resources Research, 53(12), 10701-10722.
  • Berk M., Schubert O., Kroll H.-M., Buschardt B., Straub D. (2017): Ist eine Ground Truth notwendig um die Zuverlässigkeit der Umfeldwahrnehmung zu bestimmen? 8. Tagung Fahrerassistenz - Einführung hochautomatisiertes Fahren, 22. -23. November, TÜV SÜD, FTM TUM, München
  • 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