Transfer Learning in Automotive Radar Using Simulated Training Data Sets

Felix Rutz, Ralph Rasshofer, Erwin Biebl

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

Abstract

For a reliable detection and classification of road users in modern automotive radar systems, latest research introduces machine-learning (ML) based algorithms in competition to implementations based on the classical radar signal processing chain. Suitable training datasets for ML systems based on real-world radar measurements are however either rarely available or lack the specific radar raw data. A training approach based on transfer-learning methods from data generated by a simulation framework is presented for the range-Doppler-representation of radar measurement data. In particular, the impact of dataset size and sample quality in relation to the performance of the ML system in the radar domain is examined.

Original languageEnglish
Title of host publication2023 24th International Radar Symposium, IRS 2023
PublisherIEEE Computer Society
ISBN (Electronic)9783944976341
DOIs
StatePublished - 2023
Event24th International Radar Symposium, IRS 2023 - Berlin, Germany
Duration: 24 May 202326 May 2023

Publication series

NameProceedings International Radar Symposium
Volume2023-May
ISSN (Print)2155-5753

Conference

Conference24th International Radar Symposium, IRS 2023
Country/TerritoryGermany
CityBerlin
Period24/05/2326/05/23

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