Investigating Implicit and Explicit Communication of Highly Automated Vehicles in Japan: How do Light-band eHMIs affect Pedestrians' Willingness to Cross, Trust and Perceived Safety?

In the near future, pedestrians will face highly automated vehicles on the roads. Highly automated vehicles (HAVs) should have safety-enhancing communication tools to guarantee traffic safety, e.g., vehicle kinematics and external human-machine interfaces (eHMIs). Pedestrians, as highly vulnerable road users, depend on communication with HAVs. Miscommunication between pedestrians and HAVs could quickly result in accidents, and this, in turn, could cause severe impairments for pedestrians. Light-band eHMIs have the potential to enhance traffic safety. However, eHMIs have been less explored in Japan so far. As a first-time approach, this experimental online study shed light on the effect of a light-band eHMI on Japanese pedestrians (N=99). In short video sequences, the participants interacted with two differently sized HAVs equipped with light-band eHMI. We investigated the effect of vehicle size (small vs. large), eHMI status (no eHMI vs. static eHMI vs. dynamic eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrians' willingness to cross, trust, and perceived safety. To investigate possible side effects of eHMIs, we also included experimental conditions in which the eHMI mismatched the vehicle's kinematics. Results revealed that Japanese were more willing to cross the street and indicated higher trust- and safety ratings when they received information about the vehicle's intention and automation status (dynamic eHMI) compared to when they received no information (no eHMI) or only about the vehicle automation status (static eHMI). Surprisingly, Japanese participants tended to rely on the eHMI when there was mismatching information between eHMI and vehicle kinematics. Overall, we concluded that light-band eHMIs could contribute to a safe future interaction between pedestrians and HAVs in Japan under the requirement that the eHMI is in accordance with vehicle kinematics.

Development of a human machine interface for robotically assisted surgery optimized for laparoscopic workflows.

INTRODUCTION: In robotic-assisted surgery (RAS), the input device is the primary site for the flow of information between the user and the robot. Most RAS systems remove the surgeon's console from the sterile surgical site. Beneficial for performing lengthy procedures with complex systems, this ultimately lacks the flexibility that comes with the surgeon being able to remain at the sterile site. METHODS: A prototype of an input device for RAS is constructed. The focus lies on intuitive control for surgeons and a seamless integration into the surgical workflow within the sterile environment. The kinematic design is translated from the kinematics of laparoscopic surgery. The input device uses three degrees of freedom from a flexible instrument as input. The prototype's performance is compared to that of a commercially available device in an evaluation. Metrics are used to evaluate the surgeons' performance with the respective input device in a virtual environment implemented for the evaluation. RESULTS: The evaluation of the two input devices shows statistically significant differences in the performance metrics. With the proposed prototype, the surgeons perform the tasks faster, more precisely, and with fewer errors. CONCLUSION: The prototype is an efficient and intuitive input device for surgeons with laparoscopic experience. The placement in the sterile working area allows for seamless integration into the surgical workflow and can potentially enable new robotic approaches.

Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller.

We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller's occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans' preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user's physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment.

[ADAS and automation-a relevant contribution to maintaining mobility of older drivers?] / Fahrerassistenzsysteme und automatisiertes Fahren ­ ein relevanter Beitrag zum Erhalt der Mobilität im Alter?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have a high level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be a priority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in a critical situation.

Interactive Deformable Colored Sound Display Achieved with Electrostrictive Fluoropolymer and Halide Perovskite.

The association of color and sound helps human cognition through a synergetic effect like intersensory facilitation. Although soft human-machine interfaces (HMIs) providing unisensory expression have been widely developed, achieving synchronized optic and acoustic expression in one device system has been relatively less explored. It is because their operating principles are different in terms of materials, and implementation has mainly been attempted through structural approaches. Here, a deformable sound display is developed that generates multiple colored lights with large sound at low input voltage. The device is based on alternating-current electroluminescence (ACEL) covered with perovskite composite films. A sound wave is created by a polymer matrix of the ACEL, while simultaneously, various colors are produced by the perovskite films and the blue electroluminescence (EL) emitted from the phosphors in the ACEL. By patterning different colored perovskite films onto the ACELs, associating the color and the sound is successfully demonstrated by a piano keyboard and a wearable interactive device.

MXene-Deposited Melamine Foam-Based Iontronic Pressure Sensors for Wearable Electronics and Smart Numpads.

Iontronic pressure sensors hold significant potential to emerge as vital components in the field of flexible and wearable electronics, addressing a variety of applications spanning wearable technology, health monitoring systems, and human-machine interactions. This study introduces a novel iontronic pressure sensor structure based on a seamlessly deposited Ti3C2Tx MXene layer onto highly porous melamine foam as parallel plate electrodes and an ionically conductive electrolyte of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide/thermoplastic polyurethane coupled with carbon cloth as current collecting layers for improved sensitivity and high mechanical stability of more than 7000 cycles. MXene-deposited melamine foam-based iontronic pressure sensors (MIPS) showed a high sensitivity of 5.067 kPa-1 in the range of 45-60 kPa and a fast response/recovery time of 28/18 ms, respectively. The high sensitivity, high mechanical stability, and fast response/recovery time of the designed sensor make them highly promising candidates for real-time body motion monitoring. Moreover, sensors are employed as a smart numpad for integration into advanced ATM security systems utilizing machine learning algorithms. This research marks a significant advance in iontronic pressure sensor technology, offering promising avenues for application in wearable electronics and security systems.

Virtual/augmented reality-based human-machine interface and interaction modes in airport control towers.

The concept of an innovative human-machine interface and interaction modes based on virtual and augmented reality technologies for airport control towers has been developed with the aim of increasing the human performances and situational awareness of air traffic control operators. By presenting digital information through see-through head-mounted displays superimposed over the out-of-the-tower view, the proposed interface should stimulate controllers to operate in a head-up position and, therefore, reduce the number of switches between a head-up and a head-down position even in low visibility conditions. This paper introduces the developed interface and describes the exercises conducted to validate the technical solutions developed, focusing on the simulation platform and exploited technologies, to demonstrate how virtual and augmented reality, along with additional features such as adaptive human-machine interface, multimodal interaction and attention guidance, enable a more natural and effective interaction in the control tower. The results of the human-in-the-loop real-time validation exercises show that the prototype concept is feasible from both an operational and technical perspective, the solution proves to support the air traffic controllers in working in a head-up position more than head-down even with low-visibility operational scenarios, and to lower the time to react in critical or alerting situations with a positive impact on the human performances of the user. While showcasing promising results, this study also identifies certain limitations and opportunities for refinement, aimed at further optimising the efficacy and usability of the proposed interface.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction.

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

Ultrahigh Sensitivity for Thermographic Human-Machine Interface via Precious Metals Atomic Layer Deposition on V-MXene: Computational and Experimental Exploration.

Global healthcare based on the Internet of Things system is rapidly transforming to measure precise physiological body parameters without visiting hospitals at remote patients and associated symptoms monitoring. 2D materials and the prevailing mood of current ever-expanding MXene-based sensing devices motivate to introduce first the novel iridium (Ir) precious metal incorporated vanadium (V)-MXene via industrially favored emerging atomic layer deposition (ALD) techniques. The current work contributes a precise control and delicate balance of Ir single atomic forms or clusters on the V-MXene to constitute a unique precious metal-MXene embedded heterostructure (Ir-ALD@V-MXene) in practical real-time sensing healthcare applications to thermography with human-machine interface for the first time. Ir-ALD@V-MXene delivers an ultrahigh durability and sensing performance of 2.4% °C-1 than pristine V-MXene (0.42% °C-1), outperforming several conventionally used MXenes, graphene, underscoring the importance of the Ir-ALD innovative process. Aberration-corrected advanced ultra-high-resolution transmission/scanning transmission electron microscopy confirms the presence of Ir atomic clusters on well-aligned 2D-layered V-MXene structure and their advanced heterostructure formation (Ir-ALD@V-MXene), enhanced sensing mechanism is investigated using density functional theory (DFT) computations. A rational design empowering the Ir-ALD process on least explored V-MXene can potentially unfold further precious metals ALD-process developments for next-generation wearable personal healthcare devices.

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.

Selecting the Appropriate Speed for Rotational Elements in Human-Machine Interfaces: A Quantitative Study.

The motion of rotation, which served as a dynamic symbol within human-computer interfaces, has garnered extensive attention in interface and graphic design. This study aimed to establish speed benchmarks for interface design by exploring visual system preferences for the perception of both simple and complex rotating icons within the velocity range of 5-1800 degrees per second. The research conducted two experiments with 12 participants to examine the observers' just noticeable difference in speed (JNDS) and perceived speed for rotational icons. Experiment one measured the JNDS over eight-speed levels using a constant stimulus method, achieving a range of 14.9-29%. Building on this, experiment two proposed a sequence of speeds within the given range and used a rating scale method to assess observers ' subjective perception of the speed series' rapidity. The findings indicated that speed increases impacted the ability to differentiate between speeds; key points for categorizing low, medium, and high speeds were identified at 10, 180, and 720 degrees/s, respectively. Shape complexity was found to modulate the visual system's perception of actual speed, such that at rotation speeds above 180 degrees/s, complex icons appeared to rotate faster than simpler ones. Most importantly, the study applied quantitative methods and metrology to interface design, offering a more scientific approach to the design workflow.

The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation.

The effectiveness of the human-machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers' takeover time, performance, and eye glance behavior. Drivers' behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

Hazard warning modalities and timing thresholds for older drivers with impaired vision.

PURPOSE: We examined collision warning systems with different modalities and timing thresholds, assessing their impact on responses to pedestrian hazards by drivers with impaired contrast sensitivity (ICS). METHODS: Seventeen ICS (70-84 y, median CS 1.35 log units) and 17 normal vision (NV: 68-73 y, median CS 1.95) participants completed 6 city drives in a simulator with 3 bimodal warnings: visual-auditory, visual-directional-tactile, and visual-non-directional-tactile. Each modality had one drive with early and one with late warnings, triggered at 3.5 s and 2 s time-to-collision, respectively. RESULTS: ICS participants triggered more early (43 vs 37 %) and late warnings (12 vs 6 %) than NV participants and had more collisions (3 vs 0 %). Early warnings reduced time to fixate hazards (late 1.9 vs early 1.2 s, p < 0.001), brake response times (2.8 vs 1.8 s, p < 0.001) and collision rates (1.2 vs 0.02 %). With late warnings, ICS participants took 0.7 s longer to brake than NV (p < 0.001) and had an 11 % collision rate (vs 0.7 % with early warnings). Non-directional-tactile warnings yielded the lowest collision rates for ICS participants (4 vs auditory 12 vs directional-tactile 15.2 %) in late warning scenarios. All ICS participants preferred early warnings. CONCLUSIONS: While early warnings improved hazard responses and reduced collisions for ICS participants, late warnings did not, resulting in high collision rates. In contrast, both early and late warnings were helpful for NV drivers. Non-directional-tactile warnings were the most effective in reducing collisions. The findings provide insights relevant to the development of hazard warnings tailored for drivers with impaired vision.

Observing Proton-Electron Mixed Conductivity in Graphdiyne.

Mixed conducting materials with both ionic and electronic conductivities have gained prominence in emerging applications. However, exploring material with on-demand ionic and electronic conductivities remains challenging, primarily due to the lack of correlating macroscopic conductivity with atom-scale structure. Here, the correlation of proton-electron conductivity and atom-scale structure in graphdiyne is explored. Precisely adjusting the conjugated diynes and oxygenic functional groups in graphdiyne yields a tunable proton-electron conductivity on the order of 103. In addition, a wet-chemistry lithography technique for uniform preparation of graphdiyne on flexible substrates is provided. Utilizing the proton-electron conductivity and mechanical tolerance of graphdiyne, bimodal flexible devices serving as capacitive switches and resistive sensors are created. As a proof-of-concept, a breath-machine interface for sentence-based communication and self-nursing tasks with an accuracy of 98% is designed. This work represents an important step toward understanding the atom-scale structure-conductivity relationship and extending the applications of mixed conducting materials to assistive technology.

Objective assessment of postural ergonomics in neurosurgery: integrating wearable technology in the operating room.

OBJECTIVE: Physical stress associated with the static posture of neurosurgeons over prolonged periods can result in fatigue and musculoskeletal disorders. Objective assessment of surgical ergonomics may contribute to postural awareness and prevent further complications. This pilot study examined the feasibility of using wearable technology as a biofeedback tool to address this gap. METHODS: Ten neurosurgeons, including 5 attendings (all faculty) and 5 trainees (1 fellow, 4 residents), were recruited and equipped with two wearable sensors attached to the back of their head and their upper back. The sensors collected the average time spent in extended (≤ -10°), neutral (> -10° and < 10°), and flexed (≥ 10°) static postures (undetected activity for more than 10 seconds) during spine and cranial procedures. Feasibility outcomes aimed for more than 70% of accurate data collection. Exploratory outcomes included the comparison of postural variability within and between participants adjusted to their demographics excluding nonrelated surgical activities, and postoperative self-assessment surveys. RESULTS: Sixteen (80%) of 20 possible recordings were successfully collected and analyzed from 11 procedures (8 spine, 3 cranial). Surgeons maintained a static posture during 52.7% of the active surgical time (mean 1.58 hrs). During spine procedures, all surgeons used an exoscope while standing, leading to a significantly longer time spent in a neutral static posture (p < 0.001, partial η2 = 0.14): attendings remained longer in a neutral static posture (36.4% ± 15.3%) than in the extended (9% ± 6.3%) and flexed (5.7% ± 3.4%) static postures; trainees also remained longer in a neutral static posture (30.2% ± 13.8%) than in the extended (11.1% ± 6.3%) and flexed (11.9% ± 6.6%) static postures. During cranial procedures, surgeons intermittently transitioned between standing/exoscope use and sitting/microscope use, with trainees spending a shorter time in a neutral static posture (16.3% vs 48.5%, p < 0.001) and a longer time in a flexed static posture (18.5% vs 2.7%, p < 0.001) compared with attendings. Additionally, longer cranial procedures correlated with surgeons spending a longer time (r = 0.94) in any static posture (extended, flexed, and neutral), with taller surgeons exhibiting longer periods in flexed and extended static postures (r = 0.86). Postoperative self-assessment revealed that attendings perceived spine procedures as more difficult than trainees (p = 0.029), while trainees found cranial procedures to be of greater difficulty than spine procedures (p = 0.012). Attendings felt more stressed (p = 0.048), less calmed (p = 0.024), less relaxed (p = 0.048), and experienced greater stiffness in their upper body (p = 0.048) and more shoulder pain (p = 0.024) during cranial versus spine procedures. CONCLUSIONS: Wearable technology is feasible to assess postural ergonomics and provide objective biofeedback to neurosurgeons during spine and cranial procedures. This study showed reproducibility for future comparative protocols focused on correcting posture and surgical ergonomic education.

EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W).

The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury.

Objective. Chronic motor impairments of arms and hands as the consequence of a cervical spinal cord injury (SCI) have a tremendous impact on activities of daily life. A considerable number of people however retain minimal voluntary motor control in the paralyzed parts of the upper limbs that are measurable by electromyography (EMG) and inertial measurement units (IMUs). An integration into human-machine interfaces (HMIs) holds promise for reliable grasp intent detection and intuitive assistive device control.Approach. We used a multimodal HMI incorporating EMG and IMU data to decode reach-and-grasp movements of groups of persons with cervical SCI (n = 4) and without (control, n = 13). A post-hoc evaluation of control group data aimed to identify optimal parameters for online, co-adaptive closed-loop HMI sessions with persons with cervical SCI. We compared the performance of real-time, Random Forest-based movement versus rest (2 classes) and grasp type predictors (3 classes) with respect to their co-adaptation and evaluated the underlying feature importance maps.Main results. Our multimodal approach enabled grasp decoding significantly better than EMG or IMU data alone (p<0.05). We found the 0.25 s directly prior to the first touch of an object to hold the most discriminative information. Our HMIs correctly predicted 79.3 ± STD 7.4 (102.7 ± STD 2.3 control group) out of 105 trials with grand average movement vs. rest prediction accuracies above 99.64% (100% sensitivity) and grasp prediction accuracies of 75.39 ± STD 13.77% (97.66 ± STD 5.48% control group). Co-adaption led to higher prediction accuracies with time, and we could identify adaptions in feature importances unique to each participant with cervical SCI.Significance. Our findings foster the development of multimodal and adaptive HMIs to allow persons with cervical SCI the intuitive control of assistive devices to improve personal independence.

Investigating the impact of HMI on drivers' merging performance in intelligent connected vehicle environment.

Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human-machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators.

Smart Printed Triboelectric Wearable Sensor with High Performance for Glove-Based Motion Detection.

In a world increasingly driven by data, wearable triboelectric nanogenerators (TENGs) offer a convenient way to monitor and collect information about human body motions. To meet the demands of the large-scale production of wearable TENGs, material selection to realize a high conversion efficiency and simplify the fabrication process remains a challenge. To address these issues, we present a simple-structured wearable printed arc-shaped triboelectric sensor (PATS) for finger motion detection by leveraging inkjet printing technology. In this regard, pressure sensors composed of diverse materials based on dielectric-dielectric and metal-dielectric structures in contact-separation mode were fabricated and compared. Thanks to the unique characteristics of the silver nanoparticle (Ag-NP)-printed layer and silicon rubber (SR), the SR-Ag PATS shows a high peak-to-peak voltage of 14.15 V and a short-circuit current of 0.78 µA. The proposed sensor with the capability of accurately identifying finger motions at various bending angles suggests promising application potential in glove-based human-machine interface (HMI) systems.