TY - GEN
T1 - Evaluation on Comfortable Arousal in Autonomous Driving Using Physiological Indexes
AU - Sakashita, Naoki
AU - Jadram, Narumon
AU - Sripian, Peeraya
AU - Laohakangvalvit, Tipporn
AU - Sugaya, Midori
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - At level 3 of autonomous driving, the driver has to take over driving when the system requires. In automatic driving, the arousal level tends to decrease. Drowsiness or less arousal is the leading cause of car accidents. For safety, it is necessary to increase the arousal level before driving. Moreover, due to the emotional state effect on the driving performance, it is important to consider comfort while improving the driver's arousal level. Previous studies proposed the comfortable arousal model based on physiological signals to evaluate arousal and comfort during autonomous driving. However, the accuracy evaluation using this model has not been sufficiently performed. This study aims to construct a more accurate and reliable comfortable arousal model. We explore various physiological indexes and calculate feature importance using the random forest method to achieve our goal. Then we compare and validate the evaluation accuracy with the subjective evaluation score against the previous comfortable model proposed. The result shows that the proposed method has more accurate than the methods of the previous method. However, the improved accuracy is still not very high, so we need to consider creating a comfortable arousal model.
AB - At level 3 of autonomous driving, the driver has to take over driving when the system requires. In automatic driving, the arousal level tends to decrease. Drowsiness or less arousal is the leading cause of car accidents. For safety, it is necessary to increase the arousal level before driving. Moreover, due to the emotional state effect on the driving performance, it is important to consider comfort while improving the driver's arousal level. Previous studies proposed the comfortable arousal model based on physiological signals to evaluate arousal and comfort during autonomous driving. However, the accuracy evaluation using this model has not been sufficiently performed. This study aims to construct a more accurate and reliable comfortable arousal model. We explore various physiological indexes and calculate feature importance using the random forest method to achieve our goal. Then we compare and validate the evaluation accuracy with the subjective evaluation score against the previous comfortable model proposed. The result shows that the proposed method has more accurate than the methods of the previous method. However, the improved accuracy is still not very high, so we need to consider creating a comfortable arousal model.
KW - Arousal
KW - Autonomous driving
KW - Comfort
KW - Physiological signals
UR - http://www.scopus.com/inward/record.url?scp=85131123431&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131123431&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05643-7_20
DO - 10.1007/978-3-031-05643-7_20
M3 - Conference contribution
AN - SCOPUS:85131123431
SN - 9783031056420
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 305
EP - 316
BT - Artificial Intelligence in HCI - 3rd International Conference, AI-HCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
A2 - Degen, Helmut
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Artificial Intelligence in HCI, AI-HCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -