CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-03-31
Cited: 0
Clicked: 5933
Xin HE, Zhe ZHANG, Li XU, Jiapei YU. Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(3): 452-462.
@article{title="Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder",
author="Xin HE, Zhe ZHANG, Li XU, Jiapei YU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="3",
pages="452-462",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000667"
}
%0 Journal Article
%T Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder
%A Xin HE
%A Zhe ZHANG
%A Li XU
%A Jiapei YU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 3
%P 452-462
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000667
TY - JOUR
T1 - Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder
A1 - Xin HE
A1 - Zhe ZHANG
A1 - Li XU
A1 - Jiapei YU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 3
SP - 452
EP - 462
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000667
Abstract: driving behavior normalization is important for a fair evaluation of the driving style. The longitudinal control of a vehicle is investigated in this study. The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition. Unlike the model-based approach as in previous work, where a necessary driver model is employed to conduct the driving cycle test, the approach we propose directly normalizes the driving behavior using an auto-encoder (AE) when following a standard speed profile. To ensure a positive correlation between the vehicle speed and driving behavior, a gate constraint is imposed in between the encoder and decoder to form a gated AE (gAE). This approach is model-free and efficient. The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis. Simulations are conducted to verify the effectiveness of the proposed scheme.
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