Verma, B. and Choudhary, A. (2018) A Framework for Driver Emotion Recognition Using Deep Learning and Grassmann Manifolds. 2018 21st International Conference on Intelligent Transportation Systems, Maui, 4-7 November 2018, 1421-1426.
https://doi.org/10.1109/ITSC.2018.8569461

has been cited by the following article:

• TITLE: Driver State Detection Based on Cardiovascular System and Driver Reaction Information Using a Graphical Model

  AUTHORS: Thanh Tung Nguyen, Hirofumi Aoki, Anh Son Le, Hirano Akio, Kunimoto Aoki, Makoto Inagami, Tatsuya Suzuki

  KEYWORDS: Human Engineering, Driver Behavior, Bio-Signal, Safety

  JOURNAL NAME: Journal of Transportation Technologies, Vol.11 No.2, March 3, 2021

  ABSTRACT: Traffic accidents are mainly caused by human error. In an aging society, the number of accidents attributed to elderly drivers is increasing. One noteworthy reason for this is operation misapplication. Studies have been conducted on the use of human-machine interfaces (HMIs) to inform the driver when he or she makes an error and encourage appropriate actions. However, the driver state during the erroneous action has not been investigated. The purpose of this study is to clarify the difference in the driver’s state between normal and surprising situations in a misapplication scenario, utilizing multimodal information such as biometric information and driver operation. We found significant changes in the interaction of components between the normal and the surprised driving state. The results could provide basic knowledge for the future development of a driver assistance system and driver state estimation using data acquired from multiple sensors in the vehicle.