Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RNN-based Pedestrian Crossing Prediction using Activity and Pose-related
  Features   Pedestrian crossing prediction is a crucial task for autonomous driving.
Numerous studies show that an early estimation of the pedestrian's intention
can decrease or even avoid a high percentage of accidents. In this paper,
different variations of a deep learning system are proposed to attempt to solve
this problem. The proposed models are composed of two parts: a CNN-based
feature extractor and an RNN module. All the models were trained and tested on
the JAAD dataset. The results obtained indicate that the choice of the features
extraction method, the inclusion of additional variables such as pedestrian
gaze direction and discrete orientation, and the chosen RNN type have a
significant impact on the final performance.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg