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Document type:
Zeitschriftenaufsatz 
Author(s):
Nobis, Felix; Fent, Felix; Betz, Johannes; Lienkamp, Markus 
Title:
Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation 
Abstract:
State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes art...    »
 
Keywords:
FTM Fahrdynamik 
Journal title:
Applied Sciences 
Year:
2021 
Journal volume:
11 
Journal issue:
Pages contribution:
2599 
Covered by:
Scopus 
Language:
en 
Fulltext / DOI:
Publisher:
MDPI AG 
E-ISSN:
2076-3417 
Date of publication:
15.03.2021 
TUM Institution:
Lehrstuhl für Fahrzeugtechnik