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.

Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type:why,how,why + how) × 3 (error type: false alarm, miss, correct [no error]) mixed design.Howinformation describes the vehicle's action, whilewhyinformation refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared withwhyandwhy + howinformation,howinformation was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend includingwhyinformation in AV explanations and deploying AV decision systems that are less miss-prone.

To promote drivers’ overall experiences in highly automated vehicles, we designed three objective criticality-adaptive displays: IO display highlighting Influential Objects, CO display highlighting Critical Objects, and ICO display highlighting Influential and Critical Objects differently. We conducted an online video-based survey study with 295 participants to evaluate them in varying traffic conditions. Results showed that low-trust propensity participants found ICO display more useful while high-trust propensity participants found CO displays more useful. When interacting with vulnerable road users (VRUs), participants had higher situational awareness (SA) but worse non-driving related task (NDRT) performance. Aging and CO displays also led to slower NDRT reactions. Nonetheless, older participants found displays more useful. We recommend providing different criticality-adaptive displays based on drivers’ trust propensity, age, and NDRT choice to enhance driving and NDRT performance and suggest carefully treating objects of different categories in traffic.