Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.

Background Internet intervention in Chinese University students would be a possible approach to overcome the gap between high rate of depression and high rates of underdiagnosis and undertreatment. As a popular measure of screening, the feasibility and user satisfaction of Patient Health Questionnaire-9 items for online program were tested. Methods The subjects were enrolled based on an email list from the students' office of a Chinese University, and 300 undergraduate students were randomly invited. Of which, 230 (76.7%) students were willing to participate in the study and completed the first test. After 2 weeks, a subsample of 150 (65.2%) subjects were randomly chosen to retake the test for the test–retest reliability. And 81 (35.2%) among the 230 subjects were randomly selected to undergo the Mini International Neuropsychiatric Interview (MINI) within 48 h. Among 150 subjects, 120 (52.2%) completed client satisfaction questionnaire about this online screening program. Results (1) The Cronbach's alpha was 0.80 and the test-retest reliability was 0.78; (2) the optimal cutoff score of 10 revealed a sensitivity of 0.74, specificity of 0.85, with an area under the curve of 0.897 (95% confidence interval: 0.823–0.970); (3) the mean duration of administration was 3.5 min; and (4) satisfaction with the online screening program was highly appreciated. Conclusions The results indicated potential value of the online screening program for further Internet-administrated programs of depression among Chinese University students.

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.