1 code implementation • 17 Apr 2020 • Friedrich Kruber, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch
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.
1 code implementation • 13 May 2020 • Eduardo Sánchez Morales, Friedrich Kruber, Michael Botsch, Bertold Huber, Andrés García Higuera
With these error reductions, camera-equipped UAVs are very attractive tools for traffic data acquisition.
Signal Processing
1 code implementation • 17 May 2021 • Lakshman Balasubramanian, Jonas Wurst, Michael Botsch, Ke Deng
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.
1 code implementation • 17 May 2021 • Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng
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.
1 code implementation • 5 May 2021 • Jonas Wurst, Lakshman Balasubramanian, Michael Botsch, Wolfgang Utschick
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
1 code implementation • 5 Apr 2020 • Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch
In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase.
1 code implementation • 18 Jul 2022 • Lakshman Balasubramanian, Jonas Wurst, Robin Egolf, Michael Botsch, Wolfgang Utschick, Ke Deng
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
1 code implementation • 19 Jul 2022 • Jonas Wurst, Lakshman Balasubramanian, Michael Botsch, Wolfgang Utschick
The latent space so formed is used for successful clustering and novel scenario type detection.
1 code implementation • 25 May 2023 • Marion Neumeier, Andreas Tollkühn, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.
no code implementations • 5 Apr 2020 • Friedrich Kruber, Jonas Wurst, Michael Botsch
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper.
no code implementations • 27 May 2020 • Jonas Wurst, Alberto Flores Fernández, Michael Botsch, Wolfgang Utschick
In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work.
no code implementations • 14 May 2020 • Eduardo Sánchez Morales, Michael Botsch, Bertold Huber, Andrés García Higuera
Autonomous driving is an important trend of the automotive industry.
no code implementations • 14 May 2020 • Eduardo Sánchez Morales, Michael Botsch, Bertold Huber, Andrés García Higuera
In this work, a method for high precision indoor positioning using a LiDAR is presented.
no code implementations • 25 Mar 2021 • Marion Neumeier, Andreas Tollkühn, Thomas Berberich, Michael Botsch
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.
no code implementations • 21 Apr 2023 • Marion Neumeier, Andreas Tollkühn, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
no code implementations • 12 May 2023 • Marion Neumeier, Andreas Tollkühn, Michael Botsch, Wolfgang Utschick
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways.
no code implementations • 16 Aug 2023 • Marion Neumeier, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios.