Abstract
Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.
Highlights
Imagine driving through a city during rush hour on the way to an important meeting
Multivariate Prediction of Frust and NoFrust Drives Based on action units (AUs) Data The average classification accuracy for the Frust vs. the noFrust condition using the multivariate approach based on the AU activations was 62.0% (SD = 9.6%) and the mean F1 score was 0.617 (SD = 0.097)
We present our results in the form of TPR and FPR
Summary
Imagine driving through a city during rush hour on the way to an important meeting. You started a little late and realize that with the dense traffic conditions, it will be hard to arrive at the meeting in time. You are becoming increasingly annoyed by the driver in front of you who is driving provocatively slowly and causes unnecessary extra stops at traffic lights. The myriads of construction sites along your way further worsen the situation. After yet another red light, you are really frustrated and it appears unbearable to you to wait behind the bus right after the light turned green. You accelerate to overtake the bus but fail to see the pedestrian crossing the street and heading to the bus
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