In the rapidly evolving field of Intelligent Transportation Systems, users’ low acceptance caused by psychological barriers has emerged as a significant obstacle to realizing the vision of a safe, efficient, and smooth mobility experience. Building on the foundation of technology acceptance research, this study introduces and validates the concept of situational acceptance within the context of automated driving, and establishes a two-dimensional model of acceptance based on a driving simulator study. This model comprises two dimensions: positivity (the tendency to accept or reject) and firmness (the strength of that tendency). Each dimension showed correlations with objective indicators, including decision-making timing, gaze fixation, pupil diameter, and eyelid opening, and these relationships were further explained by psychological theories. Furthermore, multiple machine learning models demonstrated strong classification performance on the feature set (four-class accuracy up to 0.6379 and binary accuracy up to 0.8608), further confirming the utility of the selected features. The incorporation of the two-dimensional model maintained or even enhanced classification accuracy while substantially reducing computational cost, with FLOPs decreased by up to 96%, laying the groundwork for developing a real-time, objective acceptance estimation method. Additionally, the study provides implications to enhance acceptance in interface transparency, motivational mechanisms, and adaptive personalization, offering actionable insights for developing real-time, adaptive in-vehicle systems.

This study addresses the gap in understanding drivers’ eye movement behaviors during automated lane changes and explores the application of driver monitoring in personalizing highly automated driving systems. Through a driving simulator experiment, this study thoroughly investigated the relationship between participants’ eye movement behaviors and subjective perceptions. 37 participants each experienced 48 different automated lane changes in the experiment. Their subjective feedback on situational trust and system aggressiveness was collected through questionnaires, and eye movements related to gaze, blink, saccade, fixation, pupil diameter, and eyelid opening were monitored. The analysis identified 25 significant eye movement features. The experimental results show that lower situational trust during automated lane changes correlated with increased attention to side mirrors, decreased attention to the front vehicle in the original lane, reduced blink frequency and suppressed saccade amplitude which increased the duration of effective visual information acquisition, smaller pupil diameter and greater fluctuations indicating increased cognitive load. The extracted features proved effective in building predictive models for drivers’ subjective evaluations. This study provides practical recommendations for selecting eye movement features and implementing personalized automated lane-change systems through eye movement monitoring. Future research will focus on expanding the dataset, improving predictive model performance, and exploring the application of these models.

Manipulating deformable objects is a ubiquitous task in household environments, demanding adequate representation and accurate dynamics prediction due to the objects’ infinite degrees of freedom. This work proposes DeformNet, which utilizes latent space modeling with a learned 3D representation model to tackle these challenges effectively. The proposed representation model combines a PointNet encoder and a conditional neural radiance field (NeRF), facilitating a thorough acquisition of object deformations and variations in lighting conditions. To model the complex dynamics, we employ a recurrent state-space model (RSSM) that accurately predicts the transformation of the latent representation over time. Extensive simulation experiments with diverse objectives demonstrate the generalization capabilities of DeformNet for various deformable object manipulation tasks, even in the presence of previously unseen goals. Finally, we deploy DeformNet on an actual UR5 robotic arm to demonstrate its capability in real-world scenarios.

To foster an immersive and natural human-robot interaction (HRI), the implementation of tactile perception and feedback becomes imperative, effectively bridging the conventional sensory gap. In this paper, we propose a dual-modal electronic skin (e-skin) that integrates magnetic tactile sensing and vibration feedback for enhanced HRI. The dual-modal tactile e-skin offers multi-functional tactile sensing and programmable haptic feedback, underpinned by a layered structure comprised of flexible magnetic films, soft silicone elastomer, a Hall sensor and actuator array, and a microcontroller unit. The e-skin captures the magnetic field changes caused by subtle deformations through Hall sensors, employing deep learning for accurate tactile perception. Simultaneously, the actuator array generates mechanical vibrations to facilitate haptic feedback, delivering diverse mechanical stimuli. Notably, the dual-modal e-skin is capable of transmitting tactile information bidirectionally, enabling object recognition and fine-weighing operations. This bidirectional tactile interaction framework will enhance the immersion and efficiency of interactions between humans and robots.

Automated driving is transforming the nature of passenger interaction and user experience. In highly automated vehicles, establishing passengers' trust is essential for interactions and key to achieving a positive Quality of Experience (QoE). Improving system transparency is widely employed among the various methods for calibrating trust. However, increasing transparency can also harm QoE by increasing passenger workload. This research examines this trade-off through an experiment conducted in a driving simulation environment. Our findings reveal a positive correlation between system transparency and trust but a non-monotonic correlation with the workload. This suggests that there may be better options than maximizing system transparency for QoE design, and workload management should be considered to optimize system transparency. Based on our design practice and verification, we provide suggestions for improving system transparency, including information integration, synergy, feedback, and auxiliary prediction.