no code implementations • 15 Aug 2023 • Maximilian Schäfer, Kun Zhao, Anton Kummert
In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy.
no code implementations • 8 Jun 2023 • Marco Braun, Moritz Luszek, Mirko Meuter, Dominic Spata, Kevin Kollek, Anton Kummert
These approaches derive scene dynamics implicitly by correlating novel input and memorized data utilizing ConvNets.
no code implementations • 5 Jun 2023 • Marco Braun, Moritz Luszek, Jan Siegemund, Kevin Kollek, Anton Kummert
In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions.
no code implementations • 22 May 2023 • Marco Braun, Alessandro Cennamo, Markus Schoeler, Kevin Kollek, Anton Kummert
State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation.
no code implementations • 19 Dec 2022 • Frederik Hasecke, Pascal Colling, Anton Kummert
We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation.
no code implementations • 20 Jun 2022 • Frederik Hasecke, Martin Alsfasser, Anton Kummert
To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data.
no code implementations • 18 Jan 2022 • Maximilian Schäfer, Kun Zhao, Markus Bühren, Anton Kummert
Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS).
no code implementations • 29 Jan 2021 • Ido Freeman, Kun Zhao, Anton Kummert
The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction.
no code implementations • 18 Jun 2020 • Lukas Hahn, Lutz Roese-Koerner, Peet Cremer, Urs Zimmermann, Ori Maoz, Anton Kummert
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with.
no code implementations • 17 Jun 2020 • Lukas Hahn, Frederik Hasecke, Anton Kummert
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements.
no code implementations • 1 Mar 2020 • Frederik Hasecke, Lukas Hahn, Anton Kummert
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products.
no code implementations • 26 Aug 2019 • Alessandro Cennamo, Ido Freeman, Anton Kummert
Then, the signature is projected onto the class-specific statistic vector to infer the input's nature.
1 code implementation • 6 May 2019 • Lukas Hahn, Lutz Roese-Koerner, Klaus Friedrichs, Anton Kummert
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example.
1 code implementation • 19 Jan 2018 • Ido Freeman, Lutz Roese-Koerner, Anton Kummert
With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware.
no code implementations • 1 Jan 2018 • Farzin Ghorban, Javier Marín, Yu Su, Alessandro Colombo, Anton Kummert
Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware.