Self-supervised graph embeddings group variable-length traffic scenarios into semantically meaningful clusters on nuPlan, with modest quantitative accuracy and no manual labels.
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Natu- ralistic Road Traffic Data,
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Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph
Self-supervised graph embeddings group variable-length traffic scenarios into semantically meaningful clusters on nuPlan, with modest quantitative accuracy and no manual labels.