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Found 7 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.
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.
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images
In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work.
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification
In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase.
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper.
