Autonomous personal mobility vehicle (APMV) is an innovative small autonomous transportation device designed for individual use in mixed-traffic environments, such as shared spaces and indoor environments. To enhance the interaction experience between pedestrians and APMVs and to prevent potential risks, it is crucial to investigate pedestrians’ walking behaviors when interacting with APMVs and to understand the psychological processes underlying these behaviors. This study aims to investigate the causal relations between subjective evaluations of pedestrians and their walking behaviors during interactions with an APMV equipped with an external human-machine interface (eHMI). An experiment of pedestrian-APMV interaction was conducted with 42 pedestrian participants, in which various eHMIs on the APMV were designed to induce participants to experience different levels of subjective evaluations and generate the corresponding walking behaviors. Based on the hypothesized model of the pedestrian’s cognition-decision-behavior process, the results of causal discovery align with the previously proposed model. Furthermore, this study further analyzes the direct and total causal effects of each factor and investigates the causal processes affecting several important factors in the field of human-vehicle interaction, such as situation awareness, trust in vehicle, risk perception, hesitation in decision making, and walking behaviors.

Motion sickness is a common issue for passengers in public transportation, particularly when engaging in visual tasks using handheld devices. This study investigates the feasibility of capturing individual motion sickness progression in bus passengers using a six-degree-of-freedom subjective vertical conflict (6DoF-SVC) model driven solely by motion data from a handheld tablet. Fifteen participants performed a visual search task on a tablet while riding a public bus, during which motion data from both a head-mounted inertial measurement unit (IMU) and a tablet-integrated IMU were recorded. Individual motion sickness severity was assessed using the Motion Illness Symptoms Classification (MISC). Model parameters were individually identified under different data availability conditions, and estimation accuracy was evaluated using mean absolute error. The results showed that estimation accuracy using tablet motion was not significantly different from that using head motion, and that parameter estimation using data up to MISC >= 3 achieved accuracy comparable to that obtained using the entire dataset. Canonical correlation analysis revealed high correlations between head and tablet motion. These findings demonstrate that high-accuracy, individualized estimation of motion sickness severity is achievable in a real-world bus environment using only handheld device motion data, highlighting the potential for practical, sensor-light personalized monitoring systems.

Drivers with peripheral visual field defects may fail to notice pedestrians in their peripheral visual field, leading to delayed hazard awareness and increased collision risk. This study explores hanger reflex cue (HRC) as a driving assistance method for drivers with peripheral visual field defects, in which mechanical pressure is applied to specific regions of the head to facilitate anticipatory orientation toward potentially risky pedestrians and support safer driving. In a driving simulator experiment with 15 participants, we compared driving behavior with and without HRC during pedestrian encounters under simulated peripheral visual field defect. The results showed that HRC significantly shifted drivers' modal head rotation angle toward the risky pedestrian and significantly increased gaze duration toward that pedestrian. Collision occurrence was lower in the w/ HRC condition than in the w/o HRC condition, although the direct effect of HRC on collision occurrence showed only a marginal trend. A piecewise structural equation modeling analysis further suggested that HRC may contribute to collision reduction through a sequential pathway from head rotation to gaze allocation and then to collision occurrence. These findings provide preliminary evidence that HRC can support anticipatory attention allocation toward peripheral hazards and may offer a promising driving assistance method for drivers with visual field impairment.

Level 3 automated vehicles (AVs) issue a request to intervene (RtI) when the automated driving system approaches its system limitations. Although this takeover transition is safety-critical, it is usually invisible to surrounding manually driven vehicle (MV) drivers. This study proposes an external human-machine interface (eHMI) called eHMI C+O that externalizes the RtI-related takeover status of a Level 3 AV using cyan and orange light bars. A driving-simulator experiment with 40 participants examined whether the proposed eHMI supports surrounding MV drivers during AV takeover scenarios. The results showed that, compared with the ADS-status-only eHMI condition, which is similar to “Automated Driving Marker Lights,” and the no-eHMI condition, the proposed eHMI C+O significantly improved participants' understanding of the AV's driving intention, their prediction of its behavior, and their perceived sufficiency of the information presented by the AV. It also reduced hesitation, increased confidence, and promoted earlier and larger increases in time headway after the RtI was issued. In the AV accident scenario, eHMI C+O significantly reduced the odds of accident involvement for the following MV compared with the no-eHMI condition, corresponding to a 76.8

Human-robot co-carrying tasks reveal their potential in both industrial and everyday applications by leveraging the strengths of both parties. However, such collaborative tasks pose numerous challenges due to varied human intentions under time-varying workspaces, leading to human-robot conflicts. In this paper, we develop a cooperation control framework for human-robot co-carrying tasks constructed by utilizing reference generator and low-level controller to aim to achieve safe interaction and synchronized human-robot movement. Firstly, the human motion predictions are corrected in the event of prediction errors based on the conflicts measured by the interaction forces through admittance control, thereby mitigating conflict levels. Low-level controller using an energy-compensation passive velocity field control approach allows encoding the corrected motion to produce control torques for the robot. In this manner, the closed-loop robotic system is passive when the energy level exceeds the predetermined threshold, and otherwise. Furthermore, the passivity, stability, energy-compensation rate, and power flow regulation are analyzed from theoretical viewpoints. Human-in-the-loop experiments involving 18 participants have demonstrated that the proposed method significantly enhances task performance and reduces human workload, as evidenced by both objective metrics and subjective evaluations.

ObjectivesTo examine whether post-stroke lateropulsion can be corrected to the upright position by wearing REHA-glasses, which provide the wearer with front-view images rotated based on body tilt.MethodsA prospective, open-label, single-arm, exploratory, interventional study for individuals with first-ever acute stroke and spontaneous body posture rated ≥ 0.75 on the Scale for Contraversive Pushing (SCP) was conducted in a single center in Japan. The intervention was performed by three physical therapists who understood the study protocol. When the participants wore REHA-glasses, the therapist rotated the visual field of view with the intent of moving the participants' position to an upright stance. Differences in assessment between therapists were resolved by mutual agreement. The primary outcome was the change in the total SCP score between the pre-wearing and wearing periods. Adverse events included falling, cybersickness symptoms, deterioration of neurological symptoms, and mechanical problems.ResultsTwenty-four participants (mean 67.6 years) were enrolled. The total SCP score was lower during wearing than during pre-wearing: mean 0.62 (standard deviation: 0.96) vs. 2.26 (1.13); median 0.1 (interquartile range: 0-0.9) vs. 2.0 (1.3-2.5); mean difference 1.64 (95% confidence interval: 1.29-1.97), respectively, p < 0.001, Cohen's dz = 2.04. Mechanical problems, due to damage to the connection port of the headset, was the only adverse event, which did not endanger the participants' safety.ConclusionsPost-stroke lateropulsion was improved by modulating visual input using REHA-glasses.

Controllers that guarantee energetic passivity with respect to the pair of external force and velocity realize safe interaction between the mechanical system and its physical environment. However, solely adhering to energetic passivity constraints may impose fundamental limitations on control performance and, in some cases, prevent the successful execution of controlled tasks. In addition, external disturbances from the physical environment can drive the system energy level and states beyond operational regions, thereby undermining task performance and safety. In this paper, we study a robust time-varying semi-passive velocity field control to aim to relax the inherently conservative nature of fully passive control methods in a controlled manner. Specifically, the proposed control method guarantees passivity of the closed-loop system with respect to the force-velocity input-output pair when the energy level exceeds a predefined level, while permitting non-passive behaviors to preserve task performance otherwise. Furthermore, the energy level and the states of the closed-loop system are proved to converge to bounded domains even in the presence of unpredicted disturbances. Additionally, the proposed method also enables constraining power flow between the closed-loop system and its physical environment to enhance safety in the interaction process. Numerical simulation examples demonstrate the effectiveness of the proposed method.

Level 3 automated driving systems (ADSs) have attracted significant attention and are being commercialized. A level 3 ADS prompts the driver to take control by issuing a request to intervene (RtI) when its operational design domains (ODDs) are exceeded. However, complex traffic situations can cause drivers to perceive multiple potential triggers of RtI simultaneously, causing hesitation or confusion during take-over. Therefore, drivers need to clearly understand the ADS's system limitations to ensure safe take-over. This study proposes a voice-based educational human machine interface (HMI) for providing RtI trigger cues and reasons to help drivers understand ADS's system limitations. The results of a between-group experiment using a driving simulator showed that incorporating effective trigger cues and reason into the RtI was related to improved driver comprehension of the ADS's system limitations. Moreover, most participants, instructed via the proposed method, could proactively take over control of the ADS in cases, where RtI fails; meanwhile, their number of collisions was lower compared with the other RtI HMI conditions. Therefore, using the proposed method to continually enhance the driver's understanding of the system limitations of ADS through the proposed method is associated with safer and more effective real-time interactions with ADS.

Haptic shared control (HSC) is effective in teleoperation when full autonomy is limited by uncertainty or sensing constraints. However, autonomous control performance achieved by maximizing HSC strength is limited because the dynamics of the joystick and human arm affect the robot's behavior. We propose a cooperative framework coupling a joystick-independent autonomous controller with HSC. A control barrier function ignores joystick inputs within a safe region determined by the human operator in real-time, while HSC is engaged otherwise. A pilot experiment on simulated tasks with tele-operated underwater robot in virtual environment demonstrated improved accuracy and reduced required time over conventional HSC.

The shift from driver to passenger may increase the risk of Motion Sickness (MS) for Automated Vehicle (AV) occupants. Motion anticipation, which is believed to mitigate MS, relies to a large extent on visual cues. Yet, the mechanisms through which visual information influences MS remain poorly understood. This paper investigates the effect of visual translation on MS in AVs. In a simulator study, eighteen participants experienced three ‘AV rides’ with identical repetitive braking and accelerating motion on a straight road, differing only in their ‘out-the-window’ view to manipulate the amount of global optic flow. A rural, low optic flow, ride and an urban, high optic flow, ride were compared to a baseline without visual movement. Results suggest that visual translation in both the central and peripheral view, that is congruent with inertial cues, may only slightly reduce MS, as MS seemed to be slightly less in the rural and urban rides compared to the baseline. The amount of global optic flow seems to have little effect, with minimal differences in MS between the rural and urban rides. Nonetheless, it remains uncertain to what extent the type of visual content has affected MS development. A study using more generic visuals could help isolate these effects, by eliminating any recognizable visual elements, while still manipulating the global optic flow rate.

Motion sickness is critical in automated vehicle design, particularly as horizontal accelerations become more variable and pronounced. However, even within horizontal translation, whether sickness sensitivity differs between longitudinal and lateral directions remains unclear. To investigate this, we conducted a within-subjects experiment with 21 participants exposed to repeated sinusoidal accelerations while seated in a car seat without visual cues, restrained by seat belt and neck brace. Each participant experienced one combination of two acceleration levels and multiple frequencies (0.15, 0.20, 0.25, or 0.40 Hz for 3.0 m/s2; 0.15, 0.25, or 0.40 for 0.65 m/s2). Symptoms were assessed using the Motion Illness Symptoms Classification at 1-min intervals. Results revealed significantly greater symptom progression in longitudinal motion (adjusted-mean: 3.0, max: 4.75) than lateral motion (adjusted-mean: 1.67, max: 2.65), despite smaller head angular velocities. These findings provide empirical evidence for direction-specific sickness sensitivity and underscore the need to consider motion direction in vehicle design.

Autonomous personal mobility vehicles (APMVs) are novel smart mobility devices designed to provide automated individual transportation in indoor or mixed-traffic environments. However, in such environments, frequent pedestrian avoidance maneuvers may cause rapid steering adjustments and passive postural responses from passengers, thereby increasing the risk of motion sickness. This study investigated whether indicating the future driving path could mitigate motion sickness in APMV passengers. A mixed-design experiment was conducted with 40 participants under two self-reported genders as a between-subject factor (male and female), two driving paths as a between-subject factor (irregular and regular) and three driving conditions as a within-subject factor (manual driving (MD), automated driving without path indication (AD w/o path), and automated driving with path indication (AD w/ path)). Motion sickness was evaluated using the Motion Illness Symptom Classification (MISC), and head motion was assessed by calculating the delay time of participants' head yaw rate relative to APMV's yaw rate in the turning direction. The results showed that driving condition was the only factor that significantly affected both motion sickness and head-motion delay. Compared with the AD w/o path condition, both the MD and AD w/ path conditions were associated with lower motion sickness severity, longer motion sickness onset latency, and earlier head motion relative to vehicle motion. Notably, the AD w/ path condition achieved motion sickness levels comparable to those in the MD condition. Furthermore, repeated-measures correlation analysis showed significant associations between head-motion delay and all MISC metrics but the underlying physiological mechanism remains to be elucidated. These findings suggest that presenting information about future driving path can mitigate motion sickness in APMV passengers.

This paper investigates human pitch motion perception under varying visual and mechanical motion conditions and evaluates the predictive performance of the Subjective Vertical Conflict (SVC) model. Thirty-two participants experienced controlled ramp-like pitch rotation stimuli at four mean rotational rates (0.5, 1, 2.5, and 5 degrees per second) across four visual conditions (Only Vision, External Vision, Internal Vision, and No Vision) and were asked to continuously indicate their perceived rotation with respect to gravity. Results showed that participants consistently underestimated pitch rotation amplitude by around 20% and exhibited an average response delay of 0.75 seconds, while showing no significant effects of vision when comparing across the three conditions involving mechanical pitch motion.With published parameter values, the SVC model generated highly unrealistic pitch perception predictions.The SVC-VR model (omitting a visual verticality input) even predicted an incorrect perceived motion direction in the Only Vision condition. Retuning the SVC-VR model's parameters was found to significantly improve its fit to the measured subjective data, and incorporating vestibular perception thresholds in the model further enhanced accuracy.These results reveal the complexity of sensory integration in motion perception and demonstrate crucial limitations of current models and published parameter values.Future research should validate model adaptations across more diverse motion paradigms for motion perception and motion sickness to enhance prediction accuracy in virtual reality, automated vehicles, and simulator environments.

This study examines self-motion perception incorporated into motion sickness models. Research on modeling self-motion perception and motion sickness has advanced independently, though both are thought to share neural mechanisms, making the construction of a unified model opportune. Models based on the Subjective Vertical Conflict (SVC) theory, a refinement of the neural mismatch theory, have primarily focused on motion sickness, with limited validation for self-motion perception. Emerging studies have begun evaluating the perceptual validity of these models, suggesting that some models can reproduce perception in specific paradigms, while they often struggle to jointly capture motion perception and sickness. One prior study demonstrated that one of the SVC models could replicate illusory tilt during centrifugation, while others produced unrealistic responses, such as persistent tilt after motion cessation. In reality, under steady-state conditions such as being motionless, perceived motion is expected to settle to an appropriate state regardless of prior states. Based on the idea that this behavior is closely related to the equilibrium points and stability of the model dynamics, this study theoretically analyzed 6DoF-SVC models with a focus on them. Results confirmed that only one model ensures convergence from any state to a unique equilibrium point corresponding to plausible perception. In contrast, other SVC models and a conventional self-motion perception model converged to values dependent on earlier states. Further analysis showed that only this model captured both the somatogravic and Ferris wheel illusion. In conclusion, this 6DoF-SVC model unifies motion perception and sickness modeling, with theoretical convergence of the perceptual state.

Wearable vibrotactile devices are increasingly used to communicate robots' states to humans, especially in scenarios requiring physical responses like reaching for handovers. For accurate information transfer, vibrotactile signal design is critical, particularly on the forearm circumference, where anatomical variations may influence perception and behavior. Designing such signals involves selecting appropriate parameter combinations while considering intra-individual and individual variability. However, the influence of signal parameters-such as amplitude (AMP), duration of stimuli (DoS), and inter-stimulus interval (ISI)-on perceptual and behavioral responses across forearm sections remains underexplored. This study addresses this gap by statistically analyzing the relationship between these parameters and human responses across two forearm sections. The findings provide insights for designing practical forearm-wearable vibrotactile systems, enhancing human-machine interaction in applications requiring rapid, precise responses.

The increase in mixed traffic with weak lane discipline (2D mixed traffic) has attracted significant research attention. To better replicate and understand traffic with weak lane discipline, this study examined the variation in response time relative to the position of the leading vehicle, including lateral shifts. Through experiments conducted using a driving simulator and functional fitting, we demonstrated that changes in response time due to longitudinal and lateral locational shifts are well represented by linear and exponential functions, respectively. Additionally, we proposed an extended formulation of the 2D optimal velocity model (2D OVM) that incorporates variable response times, termed the 2D OVM with varying sensitivities (2D OVMVS). The stability condition was derived using a linear approximation. A comparative analysis of the phase diagrams of the 2D OVM and 2D OVMVS, along with a sensitivity analysis, revealed that the proposed 2D OVMVS exhibited a larger unstable region in the phase diagram and lower stability in stable regions than the 2D OVM. As a result, in 2D traffic with weak lane discipline, the equilibrium formation of vehicles was more susceptible to disruption. Our findings indicate that variable response times, as observed in this study, substantially influence the stability of no-lane traffic. Unlike fixed-response models, incorporating response time variability accentuates unstable tendencies. This underscores the necessity of accounting for non-uniform response time distributions in future traffic models.

With the ever-increasing spread of collaborative robotics, humans and robots working side by side in the workplace have become more common. When working on different subtasks of a bigger main task, the user and robot need only interact on a few occasions, like a handover, instead of being in constant contact. Interacting with the robot becomes a secondary focus for the human and should not distract from the main task. To free the human from having to keep their attention on the robot while ensuring an efficient handover, the use of physical stimuli is suggested. These signals allow the user to understand the robot's state while keeping their other senses free. In this research, we aim to combine two devices: one capable of informing the user about the handover position through vibrations and the other using tightening signals to inform about the state of the gripper. Through this combination, we expect an increase in handover efficiency and better human concentration on the main task. Experiments are performed to compare the completion of a handover task with and without the device.

In response to the growing need for flexibility in handling complex tasks, research on human-robot collaboration (HRC) has garnered considerable attention. Recent studies on HRC have achieved smooth handover tasks between humans and robots by adaptively responding to human states. Collaboration was further improved by conveying the state of the robot to humans via robotic interactive motion cues. However, in scenarios such as collaborative assembly tasks that require precise positioning, methods relying on motion or forces caused by interactions through the shared object compromise both task accuracy and smoothness, and are therefore not directly applicable. To address this, the present study proposes a method to convey the stiffness of the robot to a human arm during collaborative human-robot assembly tasks in a manner that does not affect the shared object or task, aiming to enhance efficiency and reduce human workload. Sixteen participants performed a collaborative assembly task with a robot, which involved unscrewing, repositioning, and reattaching a part while the robot held and adjusted the position of the part. The experiment examined the effectiveness of the proposed method, in which the robot's stiffness was communicated to a participant's forearm. The independent variable, tested within-subjects, was the stiffness presentation method, with three levels: without the proposed method (no presentation) and with the proposed method (real-time and predictive presentations). The results demonstrated that the proposed method enhanced task efficiency by shortening task completion time, which was associated with lower subjective workload scores.