Novelty detection and analysis of traffic scenario infrastructures in the latent space of a vision transformer-based triplet autoencoder

Jonas Wurst, Lakshman Balasubramanian, Michael Betsch, Wolfgang Utschick

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

8 Scopus citations

Abstract

Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The triplet training of the network is based on the connectivity graphs of the infrastructure. By using the proposed architecture, expert-knowledge is used to shape the latent space such that it incorporates a pre-defined similarity in the neighborhood relationships of an autoencoder. An ablation study on the architecture is highlighting the importance of the triplet autoencoder combination. The best performing architecture is based on vision transformers, a convolution-free attention-based network. The presented method outperforms other state-of-the-art outlier detection approaches.

Original languageEnglish
Title of host publication32nd IEEE Intelligent Vehicles Symposium, IV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1304-1311
Number of pages8
ISBN (Electronic)9781728153940
DOIs
StatePublished - 11 Jul 2021
Event32nd IEEE Intelligent Vehicles Symposium, IV 2021 - Nagoya, Japan
Duration: 11 Jul 202117 Jul 2021

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2021-July

Conference

Conference32nd IEEE Intelligent Vehicles Symposium, IV 2021
Country/TerritoryJapan
CityNagoya
Period11/07/2117/07/21

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