Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based
  Recurrent Neural Networks   To plan safe and comfortable trajectories for automated vehicles on highways,
accurate predictions of traffic situations are needed. So far, a lot of
research effort has been spent on detecting lane change maneuvers rather than
on estimating the point in time a lane change actually happens. In practice,
however, this temporal information might be even more useful. This paper deals
with the development of a system that accurately predicts the time to the next
lane change of surrounding vehicles on highways using long short-term
memory-based recurrent neural networks. An extensive evaluation based on a
large real-world data set shows that our approach is able to make reliable
predictions, even in the most challenging situations, with a root mean squared
error around 0.7 seconds. Already 3.5 seconds prior to lane changes the
predictions become highly accurate, showing a median error of less than 0.25
seconds. In summary, this article forms a fundamental step towards downstreamed
highly accurate position predictions.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con