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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Wurst, Jonas, Balasubramanian, Lakshman, Botsch, Michael, Utschick, Wolfgang
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.07.2022
Subjects
Clustering
Computer architecture
Computer Science - Computer Vision and Pattern Recognition
Knowledge representation
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2207.09120