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Driver Intention Recognition Method Based on Touch Information
ZHANG Rui
2023, 48(22):
49-59.
DOI: 10.16638/j.cnki.1671-7988.2023.022.010
With the continuous development of artificial intelligence and autonomous driving
technology, autonomous driving technology based on electrification, networking, intelligence, and
sharing has become the main development direction of the future automotive industry. However, due
to the limitations of current autonomous driving technology and relevant policies and regulations, it
can be foreseen that human-machine co-driving will exist as a transitional stage towards autonomous
driving for a long time. Therefore, this paper proposes a machine learning method based on
convolutional neural networks to address the issue of driver operation intention recognition in
human-machine co-driving. Starting from the steering wheel that the driver has the longest contact
with, the problem of operation intention recognition is transformed into a problem of image
classification and recognition through feature training of tactile pressure images, accurate
identification of different driving intentions of drivers (grip driving intentions, grip driving intentions, dangerous driving intentions) has been achieved. The simulation results show that the
accuracy of intention recognition exceeds 97%, which has important theoretical and practical value
for the future research of vehicle assisted driving systems in human-machine co-driving control.
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