Add README.md

README.md

+# Deep Convolutional Neural Networked-based Multisensor Fusion for Autonomous Vehicles
+Multisensor fusion methods are widely used in many real-world applications such
+as autonomous systems, remote sensing, video surveillance and military. The
+objective of multisensor fusion is to combine the data provided by the multiple
+sensors to achieve complementary information about the scene. The data can
+be obtained from the same sensor with several capturing parameters or multiple
+sensors.
+Deep Convolutional Neural Network (DCNN) has been developed as one of
+the main models in deep learning and successfully applied to a wide range of
+computer vision tasks showing state-of-the-art performance. For this reason,
+most multisensor fusion architectures for computer vision tasks are built based
+on DCNN. In addition, DCNNs have great potential in processing the multi-
+sensory data, which usually contains rich information in the raw data and is
+sensitive to training time as well as model size. However, the multisensor fusion
+approaches suffer from two challenges, which are (1) the feature extraction from
+various types of sensory data and (2) the selection of a suitable fusion level.
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