Sensor FDIR

Fault detection of automotive lidar

Last updated on Jan 8, 2023

Data collection and analytics to detect faults close to the lidar unit such as vibration, blocking layers on the cover. Development of testing routines and data analytics tools for the future development of fault detection algorithms.

Role: Project Manager and Technical Lead.

Duration: 2019-2022

lidar

Thomas Goelles

Scientist at Karl-Franzens-Universität Graz and Virtual Vehicle Research

My research interests include glaciology, lidar, albedo and numerical modelling of ice sheet processes

Publications

Automotive lidar, dgnss and inertia sensors for glaciological applications: first experience and future potential

Thomas Goelles, Birgit Schlager, Stefan Muckenhuber, Jakob Abermann, Christoph Gaisberger

Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Birgit Schlager, Thomas Goelles, Stefan Muckenhuber, Daniel Watzenig

MOLISENS: MObile LIdar SENsor System to exploit the potential of small industrial lidar devices for geoscientific applications

Thomas Goelles, Tobias Hammer, Stefan Muckenhuber, Birgit Schlager, Jakob Abermann, Christian Bauer, Víctor J. Expósito Jiménez, Wolfgang Schöner, Markus Schratter, Benjamin Schrei, Kim Senger

pointcloudset - Efficient analysis of large datasets of point clouds recorded over time

Thomas Goelles

Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar

Birgit Schlager, Thomas Goelles, Daniel Watzenig

`pointcloudset`: Efficient Analysis of Large Datasets of Point Clouds Recorded Over Time

Thomas Goelles, Birgit Schlager, Stefan Muckenhuber, Sarah Haas, Tobias Hammer

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Thomas Goelles, Birgit Schlager, Stefan Muckenhuber