Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Fleet Learning Architecture for Enhanced Behavior Predictions during
  Challenging External Conditions   Already today, driver assistance systems help to make daily traffic more
comfortable and safer. However, there are still situations that are quite rare
but are hard to handle at the same time. In order to cope with these situations
and to bridge the gap towards fully automated driving, it becomes necessary to
not only collect enormous amounts of data but rather the right ones. This data
can be used to develop and validate the systems through machine learning and
simulation pipelines. Along this line this paper presents a fleet
learning-based architecture that enables continuous improvements of systems
predicting the movement of surrounding traffic participants. Moreover, the
presented architecture is applied to a testing vehicle in order to prove the
fundamental feasibility of the system. Finally, it is shown that the system
collects meaningful data which are helpful to improve the underlying prediction
systems.

37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex