When to Explain: Modeling User Need for Explanations in Real-World Autonomous Driving

The integration of artificial intelligence into autonomous vehicles (AVs) raises transparency challenges that can hinder user acceptance and experience. To address when users need AV explanations, we created a large-scale dataset of 3327 diverse driving scenarios, each paired with a user-friendly explanation, and conducted an online study to survey when users need explanations. Using both scenario and user-related factors, our best-performing tree-ensemble models predicted explanation need with great performance (F1=0.72, AUC=0.82). SHAP analyses revealed that while both user- and scenario-related factors matter, factors directly related to driving (AV driving style, AV action, event cause, annual mileage, human driving style) were more contributive than general demographics and environmental factors. Our study delivers a comprehensively annotated dataset that underpins future human-AV interaction research, an explainable model that reliably predicts explanation needs, and valuable insights to inform the design of adaptive AV interfaces for superior user experiences.