Object selection is the basis of natural user–computer interaction (NUI) in a virtual environment (VE). Among the three-dimensional object selection techniques employed in virtual reality (VR), bare hand-based finger clicking interaction and ray-casting are two convenient approaches with a high level of acceptance. This study involved 14 participants, constructed a virtual laboratory environment in VR, and compared the above two finger-based interaction techniques in terms of aspects of the task performance, including the success rate, total reaction time, operational deviation, and accuracy, at different spatial positions. The results indicated that the applicable distance range of finger clicking interaction and finger ray-casting was 0.2 to 1.4 m and over 0.4 m, respectively. Within the shared applicable distance, the finger clicking interaction achieved a shorter total reaction time and higher clicking accuracy. The performance of finger clicking interaction varied remarkably at the center and edge of the horizontal field of view, while no significant difference was found among ray-casting at various horizontal azimuths. The current findings could be directly applied to the application of bare-hand interaction in VR environments.

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.

In virtual reality, users’ input and output interactions are carried out in a three-dimensional space, and bare-hand click interaction is one of the most common interaction methods. Apart from the limitations of the device, the movements of bare-hand click interaction in virtual reality involve head, eye, and hand movements. Consequently, clicking performance varies among locations in the binocular field of view. In this study, we explored the optimal interaction area of hand–eye coordination within the binocular field of view in a 3D virtual environment (VE), and implemented a bare-hand click experiment in a VE combining click performance data, namely, click accuracy and click duration, following a gradient descent method. The experimental results show that click performance is significantly influenced by the area where the target is located. The performance data and subjective preferences for clicks show a high degree of consistency. Combining reaction time and click accuracy, the optimal operating area for bare-hand clicking in virtual reality is from 20° to the left to 30° to the right horizontally and from 15° in the upward direction to 20° in the downward direction vertically. The results of this study have implications for guidelines and applications for bare-hand click interaction interface designs in the proximal space of virtual reality.

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state prediction model based on the multimodal fusion of eye and hand features, which distinguishes whether users primarily employ goal-directed attention or stimulus-driven attention during the execution of their intentions. In our experiment, we collected three types of data—eye movements, hand movements, and pupil changes—and instructed participants to complete a virtual button selection task. This setup allowed us to establish a binary ground truth label for attentional state during the execution of selection intentions for model training. To investigate the impact of different time windows on prediction performance, we designed eight time windows ranging from 0 to 4.0 s (in increments of 0.5 s) and compared the performance of eleven algorithms, including logistic regression, support vector machine, naïve Bayes, k-nearest neighbors, decision tree, linear discriminant analysis, random forest, AdaBoost, gradient boosting, XGBoost, and neural networks. The results indicate that, within the 3 s window, the gradient boosting model performed best, achieving a weighted F1-score of 0.8835 and an Accuracy of 0.8860. Furthermore, the analysis of feature importance demonstrated that the multimodal eye–hand features play a critical role in the prediction. Overall, this study introduces an innovative approach that integrates three types of multimodal eye–hand behavioral and physiological data within a virtual reality interaction context. This framework provides both theoretical and methodological support for predicting users’ attentional states within short time windows and contributes practical guidance for the design of attention-adaptive 3D interfaces. In addition, the proposed multimodal eye–hand data fusion framework also demonstrates potential applicability in other three-dimensional interaction domains, such as game experience optimization, rehabilitation training, and driver attention monitoring.

Trust and situational awareness (SA) are crucial to the adoption and safety of automated vehicles (AVs). Appropriate design of AV explanations could promote drivers’ acceptance, trust, and SA, enabling drivers to get more benefits from the technology. This study investigated the effects of error type and information type of AV explanations on drivers’ trust and SA. We recruited 300 participants for an online video study with a 3 (information type) × 2 (error type) mixed design. Linear mixed model analyses showed that compared with false alarms, misses were associated with more trust decrease after the error and more trust decrease after the post-error recovery. Compared with why information, how information was associated with lower SA generally and risked potential over-trust in false alarms. Therefore, we recommend deploying AV decision systems that are less miss-prone and including why information in AV explanations.