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Title: Modeling Driver Takeover Intention in Automated Vehicles With Attention-Based CNN Algorithm
In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors.  more » « less
Award ID(s):
1850002
PAR ID:
10411879
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
66
Issue:
1
ISSN:
2169-5067
Page Range / eLocation ID:
1607 to 1611
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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