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Open AccessArticle
Optimizing HMI for Intelligent Electric Vehicles Using BCI and Deep Neural Networks with Genetic Algorithms
by
Xinmin Jin
Xinmin Jin 1,*,
Jian Teng
Jian Teng 1 and
Shaw-mung Lee
Shaw-mung Lee 2
1
School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China
2
Technology Research Institute, Arrow Technology Company, ZhuHai 519000, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2024, 15(8), 338; https://doi.org/10.3390/wevj15080338 (registering DOI)
Submission received: 24 June 2024
/
Revised: 17 July 2024
/
Accepted: 22 July 2024
/
Published: 27 July 2024
Abstract
This study utilizes a brain—computer interface (BCI)—based deep neural network (DNN) and genetic algorithm (GA) method. This research explores the interaction design of the main control human-machine interaction interfaces (HMIs) for intelligent electric vehicles (EVs) by integrating neural network predictions with genetic algorithm optimizations. Augmented reality (AR) was incorporated into the experimental setup to simulate real driving conditions, providing participants with an immersive and realistic experience. A comparative analysis of several models including the support vector machines-genetic algorithm (SVMs-GA), decision trees-genetic algorithm (DT-GA), particle swarm optimization-genetic algorithm (PSO-GA), and deep neural network-genetic algorithm (DNN-GA) was conducted. The results indicate that the DNN-GA model exhibited superior prediction accuracy with the lowest mean squared error (MSE) of 0.22 and mean absolute error (MAE) of 0.31. Additionally, the DNN-GA model demonstrated the shortest training time of 69.93 s, making it 4.5% more efficient than the PSO-GA model and 51.8% more efficient compared to the SVMs-GA model. This research focuses on promoting an innovative and efficient machine learning hybrid model with the goal of improving the efficiency of the human-machine interaction interfaces (HMIs) interface of intelligent electric vehicles. By optimizing the accuracy and response speed, the aim is to enhance the control interface and significantly improve user experience and usability.
Share and Cite
MDPI and ACS Style
Jin, X.; Teng, J.; Lee, S.-m.
Optimizing HMI for Intelligent Electric Vehicles Using BCI and Deep Neural Networks with Genetic Algorithms. World Electr. Veh. J. 2024, 15, 338.
https://doi.org/10.3390/wevj15080338
AMA Style
Jin X, Teng J, Lee S-m.
Optimizing HMI for Intelligent Electric Vehicles Using BCI and Deep Neural Networks with Genetic Algorithms. World Electric Vehicle Journal. 2024; 15(8):338.
https://doi.org/10.3390/wevj15080338
Chicago/Turabian Style
Jin, Xinmin, Jian Teng, and Shaw-mung Lee.
2024. "Optimizing HMI for Intelligent Electric Vehicles Using BCI and Deep Neural Networks with Genetic Algorithms" World Electric Vehicle Journal 15, no. 8: 338.
https://doi.org/10.3390/wevj15080338
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