Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving
  Applications: A Review   Behaviour prediction function of an autonomous vehicle predicts the future
states of the nearby vehicles based on the current and past observations of the
surrounding environment. This helps enhance their awareness of the imminent
hazards. However, conventional behaviour prediction solutions are applicable in
simple driving scenarios that require short prediction horizons. Most recently,
deep learning-based approaches have become popular due to their superior
performance in more complex environments compared to the conventional
approaches. Motivated by this increased popularity, we provide a comprehensive
review of the state-of-the-art of deep learning-based approaches for vehicle
behaviour prediction in this paper. We firstly give an overview of the generic
problem of vehicle behaviour prediction and discuss its challenges, followed by
classification and review of the most recent deep learning-based solutions
based on three criteria: input representation, output type, and prediction
method. The paper also discusses the performance of several well-known
solutions, identifies the research gaps in the literature and outlines
potential new research directions.

27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat