TY - GEN
T1 - A driver situational awareness estimation system based on standard glance model for unscheduled takeover situations
AU - Hayashi, Hiroaki
AU - Kamezaki, Mitsuhiro
AU - Manawadu, Udara E.
AU - Kawano, Takahiro
AU - Ema, Takaaki
AU - Tomita, Tomoya
AU - Catherine, Lollett
AU - Sugano, Shigeki
N1 - Funding Information:
ACKNOWLEDGMENTS This research was supported in part by JST PRESTO Grant Number JPMJPR1754 and in part by the Research Institute for Science and Engineering, Waseda University.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Highly-automated vehicles operating in level 3 issue a takeover request (TOR) to transfer the control authority from the autonomated driving (AD) system to a human driver when they encounter system limitations. In such 'unscheduled' situations, the driver is required to immediately re-engage in the driving task both physically and cognitively, and perform suitable action, e.g. lane change. Thus, evaluating driver engagement by the AD system would lead to safe takeover. Physical engagement is easily estimated but there are few studies on evaluating cognitive engagement. In this study, we thus developed a driver situational awareness estimation system based on glance information. We first defined seven standard glance areas and driver glance classification model using a convolutional neural network. We then obtained a large amount of glance data when both safe and dangerous takeover situations (lane change) by using a driving simulator, and we derived the standard glance model including the glance area and time, in order to estimate whether driver gained enough cognitive re-engagement in real-time. To evaluate the effectiveness of the proposed model, we created a situational awareness assist system to visually indicate regions with insufficient glance. As a result, we found that the assist system drastically improved driving performance and reduced the number of accidents during takeover.
AB - Highly-automated vehicles operating in level 3 issue a takeover request (TOR) to transfer the control authority from the autonomated driving (AD) system to a human driver when they encounter system limitations. In such 'unscheduled' situations, the driver is required to immediately re-engage in the driving task both physically and cognitively, and perform suitable action, e.g. lane change. Thus, evaluating driver engagement by the AD system would lead to safe takeover. Physical engagement is easily estimated but there are few studies on evaluating cognitive engagement. In this study, we thus developed a driver situational awareness estimation system based on glance information. We first defined seven standard glance areas and driver glance classification model using a convolutional neural network. We then obtained a large amount of glance data when both safe and dangerous takeover situations (lane change) by using a driving simulator, and we derived the standard glance model including the glance area and time, in order to estimate whether driver gained enough cognitive re-engagement in real-time. To evaluate the effectiveness of the proposed model, we created a situational awareness assist system to visually indicate regions with insufficient glance. As a result, we found that the assist system drastically improved driving performance and reduced the number of accidents during takeover.
UR - http://www.scopus.com/inward/record.url?scp=85072283873&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072283873&partnerID=8YFLogxK
U2 - 10.1109/IVS.2019.8814067
DO - 10.1109/IVS.2019.8814067
M3 - Conference contribution
AN - SCOPUS:85072283873
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 798
EP - 803
BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019
Y2 - 9 June 2019 through 12 June 2019
ER -