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. 2021 Apr 29:15:641007.
doi: 10.3389/fnbot.2021.641007. eCollection 2021.

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

Huaikun Xiang  1 , Jiafeng Zhu  2 , Guoyuan Liang  2   3   4   5 , Yingjun Shen  6
Affiliations

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

Huaikun Xiang et al. Front Neurorobot. .

Abstract

Dangerous driving behavior is the leading factor of road traffic accidents; therefore, how to predict dangerous driving behavior quickly, accurately, and robustly has been an active research topic of traffic safety management in the past decades. Previous works are focused on learning the driving characteristic of drivers or depended on different sensors to estimate vehicle state. In this paper, we propose a new method for dangerous driving behavior prediction by using a hybrid model consisting of cloud model and Elman neural network (CM-ENN) based on vehicle motion state estimation and passenger's subjective feeling scores, which is more intuitive in perceiving potential dangerous driving behaviors. To verify the effectiveness of the proposed method, we have developed a data acquisition system of driving motion states and apply it to real traffic scenarios in ShenZhen city of China. Experimental results demonstrate that the new method is more accurate and robust than classical methods based on common neural network.

Keywords: Elman neural network; active vehicle safety management; auto driving scenarios; cloud model; dangerous driving behavior.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The overall framework of cloud model and Elman neural network (CM-ENN) model for dangerous driving behavior prediction.
FIGURE 2
FIGURE 2
Real-time vehicle attitude monitoring system for dangerous driving behavior analysis.
FIGURE 3
FIGURE 3
Inertial measurement unit (IMU) on vehicle.
FIGURE 4
FIGURE 4
Transformation from body frame (OXbYbZb) to the navigation frame (OXnYnZn).
FIGURE 5
FIGURE 5
Acceleration of 1D cloud model: (A) Comparison of 1D cloud models with longitudinal acceleration; (B) comparison of 1D cloud models with total acceleration.
FIGURE 6
FIGURE 6
2D cloud model with different acceleration states: (A) Slow speeding; (B) urgent to acceleration.
FIGURE 7
FIGURE 7
The structure of Elman neural network (ENN).
FIGURE 8
FIGURE 8
Flow chart of the cloud model and Elman neural network (CM-ENN) learning algorithm.
FIGURE 9
FIGURE 9
Testing data acquisition: (A) The vehicle for collecting testing data; (B) urban roads for collecting testing data.
FIGURE 10
FIGURE 10
The response curve of RMS of total acceleration aw and longitudinal acceleration ay.
FIGURE 11
FIGURE 11
The predicted aw values by two learning models and the recorded true values in a selected period of time: (A) Results of ENN; (B) results of ANN.
See this image and copyright information in PMC

References

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