53 citations · 192 across the 31 of their papers we have counts for
3 papers · 1 filter
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity
Lakshman Balasubramanian, Jonas Wurst, Michael Botsch +1
Traffic scenario categorisation is an essential component of automated driving, for e.\,g., in motion planning algorithms and their validation. Finding new relevant scenarios witho…
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch +1
An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for sce…
Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space
Marion Neumeier, Andreas Tollkühn, Thomas Berberich +1
This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides part…