The latent space so formed is used for successful clustering and novel scenario type detection.
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
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.
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.
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability.
In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work.
In this work, a method for high precision indoor positioning using a LiDAR is presented.
With these error reductions, camera-equipped UAVs are very attractive tools for traffic data acquisition.
A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose.
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper.
In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase.