Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate, Low-Latency Visual Perception for Autonomous
  Racing:Challenges, Mechanisms, and Practical Solutions   Autonomous racing provides the opportunity to test safety-critical perception
pipelines at their limit. This paper describes the practical challenges and
solutions to applying state-of-the-art computer vision algorithms to build a
low-latency, high-accuracy perception system for DUT18 Driverless (DUT18D), a
4WD electric race car with podium finishes at all Formula Driverless
competitions for which it raced. The key components of DUT18D include
YOLOv3-based object detection, pose estimation, and time synchronization on its
dual stereovision/monovision camera setup. We highlight modifications required
to adapt perception CNNs to racing domains, improvements to loss functions used
for pose estimation, and methodologies for sub-microsecond camera
synchronization among other improvements. We perform a thorough experimental
evaluation of the system, demonstrating its accuracy and low-latency in
real-world racing scenarios.

16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org