Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Predictive Models for Collision Risk Assessment in Autonomous
  Driving   In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.

7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th