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Volume 25
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10.3390/s25103224
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network

by
Chen Zhou
1,2,
Fan Zhang
1,
Edric John Cruz Nacpil
3,
Zheng Wang
4 and
Fei-Xiang Xu
1,2,*
1
Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China
2
Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Huaqiao University, Xiamen 361021, China
3
Advanced Institute of Nano Technology, Sungkyunkwan University, Suwon 16228, Republic of Korea
4
Institute of Industrial Science, The University of Tokyo, Tokyo 113-8654, Japan
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(10), 3224; https://doi.org/10.3390/s25103224
Submission received: 31 March 2025 / Revised: 15 May 2025 / Accepted: 19 May 2025 / Published: 20 May 2025
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
Versions Notes

Abstract

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver’s steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver’s input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors.
Keywords: shared steering control; driver intention prediction; CNN-GRU network shared steering control; driver intention prediction; CNN-GRU network

Share and Cite

MDPI and ACS Style

Zhou, C.; Zhang, F.; Nacpil, E.J.C.; Wang, Z.; Xu, F.-X. Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network. Sensors 2025, 25, 3224. https://doi.org/10.3390/s25103224

AMA Style

Zhou C, Zhang F, Nacpil EJC, Wang Z, Xu F-X. Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network. Sensors. 2025; 25(10):3224. https://doi.org/10.3390/s25103224

Chicago/Turabian Style

Zhou, Chen, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, and Fei-Xiang Xu. 2025. "Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network" Sensors 25, no. 10: 3224. https://doi.org/10.3390/s25103224

APA Style

Zhou, C., Zhang, F., Nacpil, E. J. C., Wang, Z., & Xu, F.-X. (2025). Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network. Sensors, 25(10), 3224. https://doi.org/10.3390/s25103224

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