Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Environment Prediction in Urban Scenes using Recurrent
  Representation Learning   A key challenge for autonomous driving is safe trajectory planning in
cluttered, urban environments with dynamic obstacles, such as pedestrians,
bicyclists, and other vehicles. A reliable prediction of the future
environment, including the behavior of dynamic agents, would allow planning
algorithms to proactively generate a trajectory in response to a rapidly
changing environment. We present a novel framework that predicts the future
occupancy state of the local environment surrounding an autonomous agent by
learning a motion model from occupancy grid data using a neural network. We
take advantage of the temporal structure of the grid data by utilizing a
convolutional long-short term memory network in the form of the PredNet
architecture. This method is validated on the KITTI dataset and demonstrates
higher accuracy and better predictive power than baseline methods.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con