Key result
A proposed detection method combining a Gaussian graphical model and logistic regression achieved 94% accuracy in distinguishing between normal and surprised driving states using cardiovascular and reaction time data.
Why the study?
Although human-machine interfaces inform drivers when making errors, the driver state during an erroneous action had not been investigated.
Observational (n=8)
No
Multimodal biometric and operational data can detect significant changes in driver state between normal and surprising situations, providing a basis for future driver assistance systems.
May support in-vehicle driver monitoring; leaves open real-world validation before clinical adoption.
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.
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Nguyễn et al. (2021) conducted an observational in Healthy elderly drivers (n=8). Surprise driving event (unexpected forward movement) vs. Normal driving state was evaluated on Accuracy of driver state detection (normal vs. surprise) on the test set. A proposed detection method combining a Gaussian graphical model and logistic regression achieved 94% accuracy in distinguishing between normal and surprised driving states using cardiovascular and reaction time data.
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