SZ
S. Zhu
Authored
5 records found
DeepEgo
Deep Instantaneous Ego-Motion Estimation Using Automotive Radar
The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from in
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The problem of 2D instantaneous ego-motion estimation for vehicles equipped with automotive radars is studied. To leverage multi-dimensional radar point clouds and exploit point features automatically, without human engineering, a novel approach is proposed that transforms ego-mo
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Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for spatial–temporal pattern extraction is proposed. The 2-D CNNs (2D-CNNs) are first applie
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Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups
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The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determ
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Contributed
1 records found
Autonomous driving can bring revolutionary advancements in transportation, by making vehicles navigate and operate independently without human intervention. To achieve this, autonomous vehicles are equipped with sensors, including cameras, Light Detection and Ranging (LiDAR), and
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