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Found 5 papers, 5 papers with code
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
The latent space so formed is used for successful clustering and novel scenario type detection.
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity
In this work, a method is proposed to address this challenge by introducing a clustering technique based on a novel data-adaptive similarity measure, called Random Forest Activation Pattern (RFAP) similarity.
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training.
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
