Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Development and Evaluation of Two Learning-Based Personalized Driver
  Models for Car-Following Behaviors   Personalized driver models play a key role in the development of advanced
driver assistance systems and automated driving systems. Traditionally,
physical-based driver models with fixed structures usually lack the flexibility
to describe the uncertainties and high non-linearity of driver behaviors. In
this paper, two kinds of learning-based car-following personalized driver
models were developed using naturalistic driving data collected from the
University of Michigan Safety Pilot Model Deployment program. One model is
developed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov
Model (HMM), and the other one is developed by combining the Gaussian Mixture
Model (GMM) and Probability Density Functions (PDF). Fitting results between
the two approaches were analyzed with different model inputs and numbers of GMM
components. Statistical analyses show that both models provide good performance
of fitting while the GMM--PDF approach shows a higher potential to increase the
model accuracy given a higher dimension of training data.

11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc