Fail-Operational Shock Detection and Correction of MEMS-based Micro-Scanning LiDAR Systems

Philipp Stelzer, Andreas Strasser, Christian Steger, Norbert Druml

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Highly automated or autonomous vehicles will be dependent on systems that have to perceive the environment to make valid decisions during the driving cycle. One of the key enablers for autonomous and highly automated vehicles will be Light Detection And Ranging (LiDAR) technology. A MicroElectro-Mechanical System (MEMS) based Micro-Scanning LiDAR is able to detect obstacles in a predefined Field-of-View (FoV). The point cloud stability of the scanned FoV is mandatory to be able to make a valid point where the obstacle is located in the scenery. Due to the fact that massive shocks can occur arbitrarily to the system, it is necessary to be able to detect and correct them as fast as possible that point cloud stability can be recovered as fast as possible. In this paper, we introduce a novel system architecture that enables a fast shock detection and correction of phase and frequency for MEMS-based MicroScanning LiDAR Systems. Our novel introduced fail-operational detection and correction system architecture was implemented in a 1D MEMS-based Micro-Scanning LiDAR FPGA platform to prove its feasibility and for performance evaluation.
Original languageEnglish
Title of host publication2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings
Number of pages6
ISBN (Electronic)978-1-7281-4842-7
Publication statusPublished - Mar 2020
Event15th IEEE Sensors Applications Symposium: SAS 2020 - Kuala Lumpur, Malaysia
Duration: 9 Mar 202011 Mar 2020

Publication series

Name2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings


Conference15th IEEE Sensors Applications Symposium
Abbreviated titleSAS 2020
CityKuala Lumpur


  • LiDAR
  • 1D MEMS Mirror
  • automated driving
  • fast shock correction
  • failure recovery system

ASJC Scopus subject areas

  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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