Modeling Perception Errors of Automated Vehicles

Martin Sigl, Christoph Schutz, Sebastian Wagner, Daniel Watzenig

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


The assessment of automated driving relies increasingly on scenario-based virtual tests to achieve sufficient test coverage. Scenarios are generally based on ground truth information. Therefore, it is necessary to reproduce the view of the environment of the automated vehicle as it is seen by the autonomous driving function in the simulation. Typically, this view is erroneous compared to the ground truth due to sensor errors. This paper presents a novel approach to cluster, identify and finally to reproduce sensor errors by maneuver-dependent statistical models for the detection of other traffic objects. Errors are classified by their static and dynamic influences and incorporated into individual error models. These are evaluated in a final step based on real driving data.

Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728189642
Publication statusPublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online


  • Autonomous Vehicles
  • Sensor Errors
  • Sensor Models
  • Sensor Systems
  • Simulation
  • Vehicle Detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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