Search Results for author: Michael Botsch

Found 13 papers, 7 papers with code

ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios

no code implementations18 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.

Representation Learning Self-Supervised Learning

Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity

1 code implementation17 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.

Autonomous Driving Motion Planning

Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios

1 code implementation17 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.

Autonomous Driving BIG-bench Machine Learning +1

Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space

no code implementations25 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.

Decision Making Trajectory Prediction

Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery

1 code implementation13 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

Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles

1 code implementation17 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.

drone-based object tracking object-detection +2

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

no code implementations5 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.