The Impact of Explanation Design on User Perception in Autonomous Driving Scenarios

Effective communication of autonomous vehicle (AV) decisions is essential for trust, safety, and acceptance. While Explainable AI (XAI) research emphasizes transparency, few studies compare rational and affective explanation styles across driving scenarios. This study conducted a 3 (driving scenario: vehicle following, lane changing, emergency braking) × 3 (explanation style: no explanation, rational explanation, affective explanation) online experiment with 270 valid participants. Participants viewed simulation videos with explanations in voice and text and rated satisfaction, perceived risk, trust, emotional experience, and intention to use. The results showed that explanation style significantly influenced users’ perceived risk, trust, and emotional experience, with affective explanations outperforming other styles across multiple dimensions. High-risk scenarios, such as emergency braking, significantly increased explanation satisfaction, indicating that users had a strong demand for information transparency in such scenarios. However, no significant interaction effect was found between explanation style and driving scenario. The findings extend XAI in AVs by underscoring the value of affective explanations and offer design implications for building transparent, trustworthy, and user-centered intelligent systems in safety-critical domains.