Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Multi-Task Learning for Joint Localization, Perception, and
  Prediction   Over the last few years, we have witnessed tremendous progress on many
subtasks of autonomous driving, including perception, motion forecasting, and
motion planning. However, these systems often assume that the car is accurately
localized against a high-definition map. In this paper we question this
assumption, and investigate the issues that arise in state-of-the-art autonomy
stacks under localization error. Based on our observations, we design a system
that jointly performs perception, prediction, and localization. Our
architecture is able to reuse computation between both tasks, and is thus able
to correct localization errors efficiently. We show experiments on a
large-scale autonomy dataset, demonstrating the efficiency and accuracy of our
proposed approach.

17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph