Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios

Jonas Wurst, Lakshman Balasubramanian, Michael Botsch, Wolfgang Utschick

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

8 Scopus citations

Abstract

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work, an expert-knowledge aided representation learning for traffic scenarios is presented. The latent space so formed is used for successful clustering and novel scenario type detection. Expert-knowledge is used to define objectives that the latent representations of traffic scenarios shall fulfill. It is presented, how the network architecture and loss is designed from these objectives, thereby incorporating expert-knowledge. An automatic mining strategy for traffic scenarios is presented, such that no manual labeling is required. Results show the performance advantage compared to baseline methods. Additionally, extensive analysis of the latent space is performed.

Original languageEnglish
Title of host publication2022 IEEE Intelligent Vehicles Symposium, IV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages484-491
Number of pages8
ISBN (Electronic)9781665488211
DOIs
StatePublished - 2022
Event2022 IEEE Intelligent Vehicles Symposium, IV 2022 - Aachen, Germany
Duration: 5 Jun 20229 Jun 2022

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2022-June

Conference

Conference2022 IEEE Intelligent Vehicles Symposium, IV 2022
Country/TerritoryGermany
CityAachen
Period5/06/229/06/22

Keywords

  • Clustering
  • Deep Learning
  • Novelty Detection
  • Scenario-Based Testing

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