Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks   Advanced driver assistance and automated driving systems should be capable of
predicting and avoiding dangerous situations. This study proposes a method to
predict potentially dangerous cut-in maneuvers happening in the ego lane. We
follow a computer vision-based approach that only employs a single in-vehicle
RGB camera, and we classify the target vehicle's maneuver based on the recent
video frames. Our algorithm consists of a CNN-based vehicle detection and
tracking step and an LSTM-based maneuver classification step. It is more
computationally efficient than other vision-based methods since it exploits a
small number of features for the classification step rather than feeding CNNs
with RGB frames. We evaluated our approach on a publicly available driving
dataset and a lane change detection dataset. We obtained 0.9585 accuracy with
side-aware two-class (cut-in vs. lane-pass) classification models. Experiment
results also reveal that our approach outperforms state-of-the-art approaches
when used for lane change detection.

36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci