Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object
  Recognition   Millimeter-wave radars are being increasingly integrated into commercial
vehicles to support new advanced driver-assistance systems by enabling robust
and high-performance object detection, localization, as well as recognition - a
key component of new environmental perception. In this paper, we propose a
novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that
extracts the location and class of objects based on further processing of the
range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D
convolutional neural networks (NN), we propose to combine several
lower-dimension NN models within our RAMP-CNN model that nonetheless approaches
the performance upper-bound with lower complexity. The extensive experiments
show that the proposed RAMP-CNN model achieves better average recall and
average precision than prior works in all testing scenarios. Besides, the
RAMP-CNN model is validated to work robustly under nighttime, which enables
low-cost radars as a potential substitute for pure optical sensing under severe
conditions.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
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The categories of this paper are: q-fin
29
The categories of this paper are: funct-an