14 citations · 38 across the 12 of their papers we have counts for
15 papers
CASPNet++: Joint Multi-Agent Motion Prediction
Maximilian Schäfer, Kun Zhao, Anton Kummert
The prediction of road users' future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (A…
Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time
Marco Braun, Moritz Luszek, Mirko Meuter +3
Current Deep Learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio-temporal relationships within o…
Quantification of Uncertainties in Deep Learning-based Environment Perception
Marco Braun, Moritz Luszek, Jan Siegemund +2
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. T…
Semantic Segmentation of Radar Detections using Convolutions on Point Clouds
Marco Braun, Alessandro Cennamo, Markus Schoeler +2
For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for…
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
Frederik Hasecke, Pascal Colling, Anton Kummert
Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks fo…
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
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.…