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.
Highlights
A report released by the World Health Organization [1] in 2020 showed that there are around 1.35 million fatalities every year because of road traffic accidents
The data set extracted with the 60-second time window was excluded and only used with the proposed model due to there being such a small sample of data
The collected data set was divided into a training set and a test set to evaluate the performance of the conventional machine learning models
Summary
A report released by the World Health Organization [1] in 2020 showed that there are around 1.35 million fatalities every year because of road traffic accidents. The factor that contributes most to a large number of accidents is human error: speeding, driving under the influence of psychoactive substances, distracted driving, etc. Klauer et al [2], in a report on the impact of driver inattention on near-crashes, showed that secondary tasks are the factors that contributed most to inattention-related accidents. The driver inattention defined in the report including secondary tasks, drowsiness, drive-related inattention to the roadway, and non-specific eye glance toward the roadway
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