Paper Info
Title
Deep Grid Fusion of Feature-Level Sensor Data with Convolutional Neural Networks
Abstract
This paper investigates neural network architectures that fuse feature-level data of radar and vision sensors in order to improve automotive environment perception for advanced driver assistance systems. Fusion is performed with occupancy grids, which incorporate sensor-specific information mapped from their individual detection lists. The fusion step is evaluated on three types of neural networks: (1) fully convolutional, (2) auto-encoder and (3) auto-encoder with skipped connections. These networks are trained to fuse radar and camera occupancy grids with the ground truth obtained from lidar scans. A detailed analysis of network architectures and parameters is performed. Results are compared to classical Bayesian occupancy fusion on typical evaluation metrics for pixel-wise classification tasks, like intersection over union and pixel accuracy. This paper shows that it is possible to perform grid fusion of feature-level sensor data with the proposed system architecture. Especially the auto-encoder architectures show significant improvements in evaluation metrics compared to classical Bayesian fusion method.
Year
DOI
Venue
2019
10.1109/ICCVE45908.2019.8965213
2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)
Keywords
Field
DocType
sensor fusion,environmental modeling,camera-radar fusion,convolutional neural network
Radar,Pattern recognition,Convolutional neural network,Computer science,Advanced driver assistance systems,Network architecture,Sensor fusion,Ground truth,Artificial intelligence,Artificial neural network,Grid
Conference
ISSN
ISBN
Citations 
2378-1289
978-1-7281-0143-9
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Gábor Balázs100.34
Walter Stechele236552.77