TY - GEN
T1 - Affective Driver State Monitoring for Personalized, Adaptive ADAS
AU - Govindarajan, Vijay
AU - Driggs-Campbell, Katherine
AU - Bajcsy, Ruzena
PY - 2018/12/7
Y1 - 2018/12/7
N2 - We seek to improve vehicle automation by using the state of the driver to develop an adaptive assistance system. We focus on the problem of measuring the driver state under varying levels of cognitive workload using affective (i.e. emotion) sensing, including thermal facial analysis and electroencephalography (EEG). This information is then used in sensor fusion and machine learning algorithms to help predict the brake reaction time of the driver, a key input in forward collision warning systems. We demonstrate the results in a pilot study, which highlights the benefits of the personalized, adaptive reaction time estimation in collision warning alert performance. A 40-50% improvement in alert precision is observed with the adaptive approach. We conclude with improvements to further strengthen the quality of the reaction time estimation and improve alert performance.
AB - We seek to improve vehicle automation by using the state of the driver to develop an adaptive assistance system. We focus on the problem of measuring the driver state under varying levels of cognitive workload using affective (i.e. emotion) sensing, including thermal facial analysis and electroencephalography (EEG). This information is then used in sensor fusion and machine learning algorithms to help predict the brake reaction time of the driver, a key input in forward collision warning systems. We demonstrate the results in a pilot study, which highlights the benefits of the personalized, adaptive reaction time estimation in collision warning alert performance. A 40-50% improvement in alert precision is observed with the adaptive approach. We conclude with improvements to further strengthen the quality of the reaction time estimation and improve alert performance.
UR - http://www.scopus.com/inward/record.url?scp=85060477088&partnerID=8YFLogxK
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U2 - 10.1109/ITSC.2018.8569585
DO - 10.1109/ITSC.2018.8569585
M3 - Conference contribution
AN - SCOPUS:85060477088
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1017
EP - 1022
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Y2 - 4 November 2018 through 7 November 2018
ER -