Exploring emotional contexts of drivers: A study using AI-generated vignettes.

Area of Science:

• Human-Computer Interaction
• Transportation Psychology
• Affective Computing

Background:

• Driver emotions critically influence driving behavior and safety, affecting attention, memory, and decision-making.
• Accurate emotion recognition is challenging due to varied emotional expressions and contextual factors.
• Limited research exists on the specific contextual influences on drivers' emotional states.

Purpose of the Study:

• To investigate how environmental and situational factors influence drivers' emotional states.
• To understand the impact of time, area, weather, surrounding conditions, and traffic on driver emotions.
• To provide insights for developing context-aware driver monitoring systems.

Main Methods:

• Utilized 64 AI-generated driving scenario vignettes (DALL-E 2).
• Recruited 238 participants via Prolific for scenario-based emotional response assessment.
• Analyzed the relationship between contextual variables and self-reported driver emotions.

Main Results:

• Rainy weather, tumultuous surroundings, and high traffic correlated with increased negative emotions.
• Driving in rural areas, morning times, or with no traffic was associated with enhanced positive emotions.
• Rainy weather specifically intensified negative emotional responses.

Conclusions:

• Contextual factors like weather, traffic, and environment significantly modulate driver emotions.
• Findings highlight the importance of context in interpreting driver emotional states.
• Results can inform the design of more effective driver monitoring systems that account for situational influences.