TY - JOUR
T1 - MODELING DRIVER TAKEOVER INTENTION IN AUTOMATED VEHICLES WITH ATTENTION-BASED CNN ALGORITHM
AU - Gupta, Shantanu
AU - Mishra, Rohit
AU - Chang, Yu Hao
AU - Ma, Zheng
AU - Ma, Fenglong
AU - Zhang, Yiqi
N1 - Publisher Copyright:
© 2022 by Human Factors and Ergonomics Society. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85181398397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181398397&partnerID=8YFLogxK
U2 - 10.1177/1071181322661303
DO - 10.1177/1071181322661303
M3 - Conference article
AN - SCOPUS:85181398397
SN - 1071-1813
VL - 66
SP - 1607
EP - 1611
JO - Proceedings of the Human Factors and Ergonomics Society
JF - Proceedings of the Human Factors and Ergonomics Society
IS - 1
T2 - 66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022
Y2 - 10 October 2022 through 14 October 2022
ER -