Authors:
Kamil Kowol
1
;
Matthias Rottmann
1
;
Stefan Bracke
2
and
Hanno Gottschalk
1
Affiliations:
1
School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, Wuppertal, Germany
;
2
Chair of Reliability Engineering and Risk Analytics, University of Wuppertal, Gaußstraße 20, Wuppertal, Germany
Keyword(s):
Uncertainty in AI, Machine Learning, Sensor Fusion, Vehicle Detection at Night.
Abstract:
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we gather the outputs and corresponding meta information for both networks. For each predicted object, the gathered information is post-processed by a gradient boosting method to produce a joint prediction of both networks. In our experiments we combine the YOLOv3 object detection network with a customized 1D radar segmentation network and evaluate our method on the nuScenes dataset. In particular we focus on night scenes, where the capability of object detection networks based on camera data is potentially handicapped. Our experiments show, that this approach of uncertainty aware fusion, which is also of very modular nature, significantly gains performance compared to single sensor baselines and is in range of specifically tailored deep learn
ing based fusion approaches.
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