Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Situation Assessment for Planning Lane Changes: Combining Recurrent
  Models and Prediction   One of the greatest challenges towards fully autonomous cars is the
understanding of complex and dynamic scenes. Such understanding is needed for
planning of maneuvers, especially those that are particularly frequent such as
lane changes. While in recent years advanced driver-assistance systems have
made driving safer and more comfortable, these have mostly focused on car
following scenarios, and less on maneuvers involving lane changes. In this work
we propose a situation assessment algorithm for classifying driving situations
with respect to their suitability for lane changing. For this, we propose a
deep learning architecture based on a Bidirectional Recurrent Neural Network,
which uses Long Short-Term Memory units, and integrates a prediction component
in the form of the Intelligent Driver Model. We prove the feasibility of our
algorithm on the publicly available NGSIM datasets, where we outperform
existing methods.

31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
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The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math