Machine learning applied to radar data: classification and semantic instance segmentation of moving road users
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Date
2021
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Abstract
Classification and semantic instance segmentation applications are rarely considered for automotive radar sensors. In current implementations, objects have to be
tracked over time before some semantic information can be extracted. In this thesis,
data from a network of 77 GHz automotive radar sensors is used to construct, train
and evaluate machine learning algorithms for the classification of moving road
users. The classification step is deliberately performed early in the process chain so
that a subsequent tracking algorithm can benefit from this extra information. For
this purpose, a large data set with real-world scenarios from about 5 h of driving
was recorded and annotated.
Given that the point clouds measured by the radar sensors are both sparse and
noisy, the proposed methods have to be sensitive to those features that discern the
individual classes from each other and at the same time, they have to be robust to
outliers and measurement errors. Two groups of applications are considered: classi-
fication of clustered data and semantic (instance) segmentation of whole scenes.
In the first category, specifically designed density-based clustering algorithms are
used to group individual measurements to objects. These objects are then used
either as input to a manual feature extraction step or as input to a neural network,
which operates directly on the bare input points. Different classifiers are trained
and evaluated on these input data.
For the algorithms of the second category, the measurements of a whole scene
are used as input, so that the clustering step becomes obsolete. A newly designed
recurrent neural network for instance segmentation of point clouds is utilized. This
approach outperforms all of the other proposed methods and exceeds the baseline
score by about ten percentage points.
In additional experiments, the performance of human test candidates on the same
task is analyzed. This study shows that temporal correlations in the data are of
great use for the test candidates, who are nevertheless outrun by the recurrent
network.
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Keywords
Radar, Machine learning, Classification, Automotive