14 citations · 38 across the 12 of their papers we have counts for
4 papers · 1 filter
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…