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: 6023
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.
[1]Al-Sultan S, Al-Bayatti AH, Zedan H, 2013. Context-aware driver behavior detection system in intelligent transportation systems. IEEE Trans Veh Technol, 62(9):4264-4275.
[2]Baldi P, 2012. Autoencoders, unsupervised learning, and deep architectures. Proc ICML Workshop on Unsupervised and Transfer, p.37-50.
[3]Cao WJ, Lin X, Zhang K, et al., 2017. Analysis and evaluation of driving behavior recognition based on a 3-axis accelerometer using a random forest approach. Proc 16th ACM/IEEE Int Conf on Information Processing in Sensor Networks, p.303-304.
[4]Chen J, Wu ZC, Zhang J, 2019. Driving safety risk prediction using cost-sensitive with nonnegativity-constrained autoencoders based on imbalanced naturalistic driving data. IEEE Trans Intell Transp Syst, 20(12):4450-4465.
[5]Filev D, Lu JB, Prakah-Asante K, et al., 2009. Real-time driving behavior identification based on driver-in-the-loop vehicle dynamics and control. Proc IEEE Int Conf on Systems, Man and Cybernetics, p.2020-2025.
[6]Hu DL, Zhao XH, Mu ZM, et al., 2013. Distinguish method of fatigue state based on driving behavior wavelet analysis. Proc 32nd Chinese Control Conf, p.3590-3596.
[7]Imamura T, Yamashita H, Zhang Z, et al., 2008. A study of classification for driver conditions using driving behaviors. Proc IEEE Int Conf on Systems, Man and Cybernetics, p.1506-1511.
[8]Kaplan S, Guvensan MA, Yavuz AG, et al., 2015. Driver behavior analysis for safe driving: a survey. IEEE Trans Intell Transp Syst, 16(6):3017-3032.
[9]Krizhevsky A, Sutskever I, Hinton GE, 2012. ImageNet classification with deep convolutional neural networks. Proc 25th Int Conf on Neural Information Processing Systems, p.1097-1105.
[10]Plöchl M, Edelmann J, 2007. Driver models in automobile dynamics application. Veh Syst Dynam, 45(7-8):699-741.
[11]Ranney TA, 1994. Models of driving behavior: a review of their evolution. Accid Anal Prev, 26(6):733-750.
[12]Shi B, Hu J, Xu L, 2015a. Automatic transmission gear prediction based on personalised transmission gear modelling. Int J Veh Syst Modell Test, 10(4):356-365.
[13]Shi B, Xu L, Hu J, et al., 2015b. Evaluating driving styles by normalizing driving behavior based on personalized driver modeling. IEEE Trans Syst Man Cybern Syst, 45(12):1502-1508.
[14]Song B, Delorme D, Werf JV, 2000. Cognitive and hybrid model of human driver. Proc IEEE Intelligent Vehicles Symp, p.1-6.
[15]TAB, 2010. China Road Traffic Accidents Statistics. Traffic Administration Bureau of the Ministry of Public Security of the People’s Republic of China (in Chinese).
[16]Tschannen M, Bachem O, Lucic M, 2018. Recent advances in autoencoder-based representation learning. https://arxiv.org/abs/1812.05069
[17]Wang WS, Han W, Na XX, et al., 2020. A probabilistic approach to measuring driving behavior similarity with driving primitives. IEEE Trans Intell Veh, 5(1):127-138.
[18]Wang YH, Ho IWH, 2018. Joint deep neural network modelling and statistical analysis on characterizing driving behaviors. Proc IEEE Intelligent Vehicles Symp, p.1-6.
[19]Xu L, Hu J, Jiang H, et al., 2015. Establishing style-oriented driver models by imitating human driving behaviors. IEEE Trans Intell Transp Syst, 16(5):2522-2530.
[20]Zhang Y, Li JJ, Guo YH, et al., 2019. Vehicle driving behavior recognition based on multi-view convolutional neural network with joint data augmentation. IEEE Trans Veh Technol, 68(5):4223-4234.
[21]Zhang YL, Lin WC, Chin YKS, 2010. A pattern-recognition approach for driving skill characterization. IEEE Trans Intell Transp Syst, 11(4):905-916.
Open peer comments: Debate/Discuss/Question/Opinion
<1>