Investigating emotion fluctuations in driving behaviors of online car-hailing drivers using naturalistic driving data

Research has shown that a driver's emotional state is closely related to the driver's driving behavior. However, most studies have focused on the impact of static and discrete emotions on driver behavior, neglecting the emotion fluctuations that can occur in real-world contexts. To address this gap, we analyzed emotion change patterns of drivers engaged in aggressive driving versus normal driving. We used online car-hailing data to identify aggressive driving, which is defined by a dynamic acceleration threshold determined from vehicle kinematics data. We used two dimensions, valence and arousal, to describe different levels of emotions by processing videos of drivers' facial expressions using FaceReader software. By analyzing the characteristics of drivers' emotional changes, we determined a six-second time window as the length of an emotion fluctuation segment. Then, we applied dynamic time warping k-means clustering as a time-series clustering method to divide the emotion fluctuations into several categories. We categorized the valence fluctuations into negative, calm, and positive and the arousal fluctuations into high and low. The clustering analysis revealed a marked fluctuation in the emotional valence of drivers during aggressive driving as opposed to the relatively smooth patterns observed during normal driving. Moreover, during aggressive driving, the driver's arousal can continuously reach higher values compared to during normal driving. This study presents a novel approach to investigating the relationship between emotions and driving behavior and provides important reference and guidance for drivers' emotional perceptions and the detection of emotion fluctuations for advanced assisted driving systems to improve driving safety.