Analysis of the Relationship Between Driver Emotions and Driving Behavior in Intersection Scenarios

In specific scenarios, changes in drivers' emotions directly influence their driving behavior. Current research on driver emotions mainly focuses on macroscopic driving behaviors and does not consider the impacts within specific driving scenarios. Therefore, aiming at the analysis of driving behaviors at intersection stopping scenarios, a deceleration feature recognition algorithm is proposed. Simultaneously, experiments are designed to study how different emotions affect deceleration characteristics at intersections and vehicle energy consumption. Firstly, to address the issue of unclear discrimination of intersection driving behavior characteristics, a fuzzy inference model for identifying driver deceleration characteristics is established based on the Fuzzy C-Means (FCM) clustering results of intersection stopping segment feature parameters. Secondly, based on actual traffic scenarios, the intersection deceleration feature model is used to analyze the impact of emotions on driving behavior at intersections. Finally, a vehicle reference energy saving rate is designed as an evaluation index to assess the impact of braking behaviors caused by different emotions on vehicle energy consumption. The results indicate that the fuzzy inference model for deceleration features accurately identifies the severity of speed changes and determines deceleration intentions. In actual intersection deceleration scenarios, compared with a happy emotional state, drivers exhibit more aggressive driving behaviors under angry emotions, reducing total braking time by 45.68% and increasing the proportion of rapid decelerations by 13.30%. Moreover, across complete intersection segments, angry emotions lead to decreased vehicle fuel efficiency, with an 87.15% reduction in the reference energy saving rate compared to happy emotions.