Open AccessMachine learning based intelligent posture design of driver
Author(s) -
Junjie Gou,
Jianbing Chuan,
Hongyan Wang,
Yang Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/3/032131
Subject(s) - computer science , artificial neural network , artificial intelligence , point cloud , key (lock) , point (geometry) , machine learning , geometry , computer security , mathematics
The automatic adjustment of the driving posture can effectively help improve the driver’s experience, and thus it is one of the important reference indicators for the design of the vehicles. This paper focuses on the intelligent adaptive driving posture using the machine learning (ML). Firstly, laser scanning was used to obtain the point cloud data of the most sold vehicles in the market. Then, the driving posture’s key parameters were screened and extracted through the big data processing method. Finally, the deep learning neural network (DNN) was applied to establish a supervised learning model to figure out the intelligent adjustment of driving posture for different vehicles. Numerical results can demonstrate the accuracy and effectiveness of the proposed method by comparing it with the actual driving experiments. The results show that the accuracy of the research is good and can provide a reference for the design of intelligent driving.