Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DRF: A Framework for High-Accuracy Autonomous Driving Vehicle Modeling   An accurate vehicle dynamic model is the key to bridge the gap between
simulation and real road test in autonomous driving. In this paper, we present
a Dynamic model-Residual correction model Framework (DRF) for vehicle dynamic
modeling. On top of any existing open-loop dynamic model, this framework builds
a Residual Correction Model (RCM) by integrating deep Neural Networks (NN) with
Sparse Variational Gaussian Process (SVGP) model. RCM takes a sequence of
vehicle control commands and dynamic status for a certain time duration as
modeling inputs, extracts underlying context from this sequence with deep
encoder networks, and predicts open-loop dynamic model prediction errors. Five
vehicle dynamic models are derived from DRF via encoder variation. Our
contribution is consolidated by experiments on evaluation of absolute
trajectory error and similarity between DRF outputs and the ground truth.
Compared to classic rule-based and learning-based vehicle dynamic models, DRF
accomplishes as high as 74.12% to 85.02% of absolute trajectory error drop
among all DRF variations.

34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph