Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extraction and Assessment of Naturalistic Human Driving Trajectories
  from Infrastructure Camera and Radar Sensors   Collecting realistic driving trajectories is crucial for training machine
learning models that imitate human driving behavior. Most of today's autonomous
driving datasets contain only a few trajectories per location and are recorded
with test vehicles that are cautiously driven by trained drivers. In particular
in interactive scenarios such as highway merges, the test driver's behavior
significantly influences other vehicles. This influence prevents recording the
whole traffic space of human driving behavior. In this work, we present a novel
methodology to extract trajectories of traffic objects using infrastructure
sensors. Infrastructure sensors allow us to record a lot of data for one
location and take the test drivers out of the loop. We develop both a hardware
setup consisting of a camera and a traffic surveillance radar and a trajectory
extraction algorithm. Our vision pipeline accurately detects objects, fuses
camera and radar detections and tracks them over time. We improve a
state-of-the-art object tracker by combining the tracking in image coordinates
with a Kalman filter in road coordinates. We show that our sensor fusion
approach successfully combines the advantages of camera and radar detections
and outperforms either single sensor. Finally, we also evaluate the accuracy of
our trajectory extraction pipeline. For that, we equip our test vehicle with a
differential GPS sensor and use it to collect ground truth trajectories. With
this data we compute the measurement errors. While we use the mean error to
de-bias the trajectories, the error standard deviation is in the magnitude of
the ground truth data inaccuracy. Hence, the extracted trajectories are not
only naturalistic but also highly accurate and prove the potential of using
infrastructure sensors to extract real-world trajectories.

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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg