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20192022
most citedAn Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

53 citations · 137 across the 8 of their papers we have counts for

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Showing 2022Show all

2 papers · 1 filter

cs.CV2022★ 12 cited

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

Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity meas…

cs.CV2022

ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios

Lakshman Balasubramanian, Jonas Wurst, Robin Egolf +3

Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the rep…