Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles

Martin Sigl, Andreas Lebherz, Christoph Schutz, Sebastian Wagner, Daniel Watzenig

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

Abstract

Virtual tests are used increasingly to assess automated driving. They are generally based on ground truth information. The perception process of automated vehicles with sensors provides a shifted, erroneous view of the environment. Within the virtual assessment process of automated driving, it is necessary to restore this sensor view for the automated driving function to ensure realistic testing. This paper presents two approaches to derive models that are able to recreate these perception errors. Both approaches are evaluated regarding their overall performance to emulate the sensor view of automated vehicles. They are able to create models that significantly reduce the difference between ground truth and an automated vehicle's sensor view of the environment. The resulting models can be used to improve the virtual assessment process.

Original languageEnglish
Title of host publicationICCVE 2022 - IEEE International Conference on Connected Vehicles and Expo
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781665416870
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Connected Vehicles and Expo: ICCVE 2022 - Florida Polytechnic University, Hybrider Event, United States
Duration: 7 Mar 20229 Mar 2022
https://iccve2022.org/

Conference

Conference2022 IEEE International Conference on Connected Vehicles and Expo
Abbreviated titleICCVE 2022
Country/TerritoryUnited States
CityHybrider Event
Period7/03/229/03/22
Internet address

Keywords

  • Autonomous Vehicles
  • Deep Learning
  • linear time invariant element
  • LSTM
  • Sensor Errors
  • Sensor Models
  • Sensor Systems
  • Simulation
  • Vehicle Detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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