Level 3 automated driving systems (ADS) 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 (ODD) 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 reason 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.

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.

As autonomous personal mobility vehicles (APMVs) are increasingly integrated into shared spaces, short-distance interactions between pedestrians and APMVs will become more frequent. To facilitate communication in shared spaces, APMVs equipped with external human-machine interfaces (eHMIs). Although the eHMI is primarily designed to communicate with pedestrians, its communication also affects the APMV passenger due to the short-distance interaction. This paper focused on the effect of passengers’ personality traits on their user experience when the APMV exhibits different eHMIs. An experiment was conducted in the field with 24 participants as APMV passengers who experienced three distinct eHMI types: eHMI-T (text-based), eHMI-NV (neutral voice-based), and eHMI-AV (affective voice-based). Through causal discovery analysis, our findings revealed that when the APMV is equipped with eHMI-T, various personality traits of passengers collectively influenced their user experience. In contrast, the eHMI-NV design demonstrated that personality traits had no direct influence on user experience. The eHMI-AV design primarily showed that agreeableness and extraversion negatively influenced concerns about drawing attention, which subsequently affected other user experience. Based on the results, this paper recommends designing different eHMIs based on the APMV ownerships, such as private or public shared APMVs.

Autonomous Personal Mobility Vehicles (APMVs) are designed to address the “last-mile” transportation challenge for everyone. When an APMV encounters a pedestrian, it uses an external Human-Machine Interface (eHMI) to negotiate road rights. Through this interaction, passengers are also passively exposed to the process. This study examines passengers' gaze behavior toward pedestrians during such interactions, focusing on whether passengers' personality traits influence their gaze patterns towards pedestrians when using different eHMI designs. When using a visual-based eHMI, which caused passengers to struggle in perceiving the communication content, the results suggested that passengers with higher Neuroticism scores, who were more sensitive to communication details, might seek cues from pedestrians' reactions. In addition, a multimodal eHMI (visual and voice) using neutral voice did not significantly affect the gaze behavior of passengers toward pedestrians, regardless of personality traits. In contrast, a multimodal eHMI using affective voice encouraged passengers with high Openness to Experience scores to focus on pedestrians' heads. In summary, this study revealed how different eHMI designs influence passengers' gaze behavior and highlighted the effects of personality traits on their gaze patterns toward pedestrians, providing new insights for personalized eHMI designs.

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.

In haptic shared control (HSC), a human operator and an autonomous controller share a common, physical control interface of a robot [1] . The strength of haptic guidance exerted by the automated controller is called the level of haptic authority (LoHA) [2] . While the high LoHA is beneficial for better safety and task performance, it is problematic when the autonomous controller is unreliable, and frequent disagreement between human and machine occurs. It is necessary to reduce the LoHA adaptively to the situation when the reliability of the autonomous controller cannot be guaranteed.

Haptic shared control (HSC) is a control approach where both a human operator and an autonomous controller exert force on a shared, physical control terminal of a robot. HSC allows the fluid combination of human intelligence and machine precision, improving task performance and reducing the human workload. When the autonomous controller is unreliable, however, the frequent disagreement between the human and the machine input adversely affects the task performance and the operator workload. Previous research showed that allowing the human operator to adjust the strength of the haptic guidance can alleviate the problem. However, the decision of when and how much to adjust the haptic guidance strength remained a challenging task for a human operator. This study proposed an approach that utilized a grip mechanism for adjusting and recommending an appropriate strength. In the proposed approach, the human operator was in charge of deciding the haptic strength via the grip angle. The autonomous controller suggested the appropriate haptic strength based on its reliability by applying proportional control to the grip angle. An experiment with a simulated remotely operated vehicle (ROV) teleoperation with HSC guidance showed that the proposed method is effective in aiding the adjustment of autonomous controller input strength, as well as in a marginal reduction in workload.

Autonomous personal mobility vehicle (APMV) is a miniaturized autonomous vehicle designed for short-distance mobility to everyone. Due to its open design, APMV’s passengers are exposed to communications between the external human-machine interface (eHMI) on APMV and pedestrians. Therefore, effective eHMI designs for APMV need to consider potential impacts of APMV-pedestrian interactions on passengers’ subjective feelings. This study from the perspective of APMV passengers discussed three eHMI designs: (1) graphical user interface (GUI)-based eHMI with text message (eHMI-T), (2) multimodal user interface (MUI)-based eHMI with neutral voice (eHMI-NV), and (3) MUI-based eHMI with affective voice (eHMI-AV). In a riding field experiment (N = 24), eHMI-T made passengers feel awkward during the “silent time” when eHMI-T conveyed information exclusively to pedestrians, not passengers. MUI-based eHMIs with voice cues showed advantages, with eHMI-NV excelling in pragmatic quality and eHMI-AV in hedonic quality. Considering passengers’ personalities and genders in APMV eHMI design is also highlighted.

Human–robot co-carrying tasks reveal their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires managing the energy level in the closed-loop systems to prevent potential dangers while also minimizing motion errors to complete the shared tasks. The collaborative tasks pose numerous challenges due to varied human intentions in adapting to workspace characteristics, 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 proposed control approach ensures that the system’s kinetic energy is compensated within a finite time interval. The passivity, stability, convergence rate of energy, 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, with improvements confirmed by statistical tests (p < 0.05) relative to baseline methods.

Advancements in robot autonomy and safety have enabled close interactions, such as object handovers, with humans. During robot-to-human handovers in assembly tasks, the robot considers the state of the human to determine its optimal handover position and timing. However, humans may struggle to focus on their primary tasks because of the need to track the robot's movement. This study aims to develop a vibrotactile interface that helps humans maintain focus on their primary tasks during object reception. The interface conveys the robot-intended handover position on the human forearm by displaying the angular direction and distance relative to the human hand via vibrotactile cues. The experimental results demonstrated that the interface allowed participants to receive objects with faster reactions and completion times, with reduced head rotation towards the robot. Participants also subjectively perceived improved performance and reduced mental workload compared with the condition without the interface.

This study takes a fresh perspective by focusing on the drivers of surrounding cars near to level 3 automated vehicles (AVs). We advocates for level 3 AVs using an external human-machine interface (eHMI) to provide high-risk warning information to drivers of surrounding cars during AVs issuing a request-to-intervene (RtI). Through a driving simulator-based subjects experiments, we have established that the proposed eHMI can assist the surrounding MV’s driver in better comprehending the AV’s driving intentions and predicting its driving behavior. This leads to increased the surrounding MV’s driver confidence in handling potential risks from the AV during the take-over period. Although we did not observe a significant impact of the proposed eHMI on the driving behavior of the MV drivers, i.e. participants, they reported a greater willingness to have AVs equipped with the proposed eHMI drive around them in their daily life.

This study aims to investigate the causal relationships from pedestrians’ psychological states to their walking behavior during interactions with an autonomous personal mobility vehicle (APMV) featuring automation capabilities ranging from SAE levels 3 to 5. A subjective experiment was conducted, where various external human-machine interfaces (eHMIs) were designed to induce participants to experience different levels of subjective feelings and generate corresponding walking behaviors. By employing a structural equation model named DirectLiNGAM to analyze the collected data for causal discovery, the results of causal discovery align with the hypothesized model of the pedestrian’s cognition-decision-behavior process. Furthermore, the experimental results have enriched the detailed causal relationships within the hypothesized model, i. e., the outcomes of situation awareness lead to a sense of danger, trust in APMV and a sense of relief; the outcomes of situation awareness, the sense of danger and trust in APMV lead to hesitation in decision-making; and the outcomes of situation awareness, the sense of danger and hesitation lead to walking behaviors.

Haptic shared control is a framework in which automation and the human pilot utilize the same operational input terminals to control a system. Both of their responses to automation failures are important. This paper describes the concept of adjusting the stiffness of the control stick based on the reliability of sensor systems. This helps human pilots perceive when to intervene and when to follow (just put their hand lightly on the stick) by changing the stiffness of the control stick. First, the haptic feedback design for changing the stiffness of a joystick is explained. Then, in the experiment, participants perform the task of keeping a remotely operated underwater vehicle stationary in front of a target in water stream using a joystick with three different haptic feedbacks: ours, constant high stiffness, and constant low stiffness. The experimental results indicate that our strategy increases control accuracy without increasing the human pilot's workload while allowing the human pilot to understand whether the human pilot or the automation should be currently in control of the task or action being performed. As the unexpected underwater environment adversely affects the sensor systems, our findings can be expected to play an important role in improving the control performance in the sea. Furthermore, it may inform the design of haptic feedback systems for low sensor visibility scenes.

Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness, especially when frequently avoiding pedestrians or obstacles in shared spaces. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Among them, models based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs v.s. those expected by the central nervous system, have been actively developed. To model motion sickness due to conflict between visual vertical information and vestibular sensation, we proposed a 6 DoF SVC-VV model which added a visually perceived vertical block into a conventional 6 DoF SVC model to predict visual vertical directions from image data simulating the visual input of a human. In a driving experiment, 27 participants rode on the APMV and experienced slalom driving with two visual conditions: looking ahead (LAD) and working with a tablet device (WAD). We verified that passengers got motion sickness while riding the APMV, and the symptoms were severer when especially working on it, by simulating the frequent pedestrian avoidance scenarios of the APMV in the experiment. In addition, the results of the experiment demonstrated that the proposed 6 DoF SVC-VV model could describe the increased motion sickness experienced when the visual vertical and gravitational acceleration directions were different.

For assembly tasks, it is essential to fix target parts firmly and accurately estimate their poses. Several rigid jigs for individual parts are frequently used in assembly factories to achieve a precise and time-efficient product assembly. However, providing customized jigs is time-consuming. In this study, to address the lack of versatility in the shapes for which jigs can be used, we developed a flexible jig with a soft membrane including transparent beads and oil with a tuned refractive index. The bead-based jamming transition was accomplished by discharging only the oil, enabling the part to be firmly fixed. Because the two cameras under the jig can capture membrane shape changes, we proposed a sensing method to estimate the orientation of the part based on the behaviors of markers created on the jig's inner surface. Through estimation experiments, the proposed system can estimate the orientation of a cylindrical object with a diameter larger than 50 mm and an RMSE of less than 3°.

Passengers (drivers) of level 3-5 autonomous personal mobility vehicles (APMV) and cars can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Many of these are based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs vs. those expected by the central nervous system. Such models are expected to be applied to autonomous driving scenarios. However, no current computational model can integrate visual vertical information with vestibular sensations. We proposed a 6 DoF SVC-VV model which add a visually perceived vertical block into a conventional six-degrees-of freedom SVC model to predict VV directions from image data simulating the visual input of a human. Hence, a simple image-based VV estimation method is proposed. As the validation of the proposed model, this paper focuses on describing the fact that the motion sickness increases as a passenger reads a book while using an AMPV, assuming that visual vertical (VV) plays an important role. In the static experiment, it is demonstrated that the estimated VV by the proposed method accurately described the gravitational acceleration direction with a low mean absolute deviation. In addition, the results of the driving experiment using an APMV demonstrated that the proposed 6 DoF SVC-VV model could describe that the increased motion sickness experienced when the VV and gravitational acceleration directions were different.

In this study, we aimed to verify whether expanding a robot’s embodiment influenced the ease of inducing behavioral changes in passersby. We conducted a field experiment to call out to passersby for disinfection in a real shop using robots with various embodiments. As a result, the expansion of the robot embodiment attracted extensive attention of passersby, and then induced behavioral changes to the desired action, such as stopping in front of the robot and disinfecting their hands. Furthermore, the results revealed that in order to induce large behavioral changes, not only the presence of the robot’s body but also its movements with strong embodiment were necessary. These results indicated that expanding the robot’s embodiment was an important factor in inducing behavioral changes in passersby.

Merging behavior requires multiple tasks such as cognition, decision-making, and driving operation. Previously, driving assistance systems, which instruct drivers on making accelerations, have been studied to support the decision-making task. The importance of improving driver comfort with adjusting system variables has been revealed through these studies. The present study aims to propose assistance methods for merging, which decreases driver's workload and difficulty in decision-making. The proposed methods recognize drivers' decision ambiguity using a decision-making model for respective drivers and instruct them on acceleration to decrease the ambiguity. First, we develop a decision-making model to predict where drivers merge based on a logistic function. Furthermore, we propose acoustic assistance methods, which instruct the acceleration and deceleration. The systems continuously calculate the optimal instruction based on driving history from the beginning of the assistance. Driving simulator experiments demonstrated that drivers' workload and decision ambiguity decreased with our proposed methods.

Recently, with the active development of automated driving systems (ADSs) corresponding to SAE automated driving level 2, the comfort of ADSs has gained significant attention. In our previous research, it was proposed that the comfort of ADSs is affected by their maneuvers and the degree of information sharing between ADS and drivers. However, even if the drivers are well-informed of the recognized traffic environment and ADS-controlled trajectory, the comfort and trust of drivers in ADSs could be insufficient. This is because each driver has distinct preferred trajectories. Although some researchers have proposed ADSs that learn driver preferences, the maneuvers presented by these systems are not easily modified, because of off-line learning. In addition, a few quantitative investigations on the subjective evaluation of comfort have been conducted. This study proposes an on-demand learning collision avoidance ADS that learns the preferred maneuvers of drivers through driver intervention. In our system, the drivers teach their preferred trajectory to the system only if they are unsatisfied with the maneuver presented by the system. The system updates the parameters and shows the learned maneuver at the next avoidance. To realize the proposed system, we applied modified risk potential functions and gain-tuning method, as well as a cost function and learning method for gradual and stable maneuver learning. Driving simulator experiments demonstrated stable trajectory learning and smooth intervention. Furthermore, the drivers were satisfied with the learned maneuvers of the ADS, and their comfort and trust in the ADS improved when using the proposed ADS.

In this study, we investigate the effectiveness of haptic shared control (HSC) for underwater vehicle operation. Underwater vehicles such as remotely operated vehicles (ROVs) and autonomous underwater vehicles are widely used for underwater maintenance and inspection. In general, ROV operators need sophisticated skills as they have to control the position and orientation of an ROV using only limited information. To improve the path tracking performance of an ROV and support its operators, we propose applying HSC by incorporating haptic feedback in a control device. We conducted a pilot experiment in a water tank using an ROV. Subjects were instructed to operate the ROV from a start line to a goal line along a desired path while watching the video image sent from the ROV camera. They were also instructed to answer questions about the colors of objects on the desired path while controlling the ROV. The results suggested that the HSC decreased their mental workload, and increased their subjective feelings of concentration on the monitor and maneuverability of the ROV while no significant difference was found in the control performance.

The vestibulo-ocular reflex (VOR) is a reflexive eye movement that is generated in a direction opposite to that of the head movement and is affected by mental workload (MWL). In a previous study, VOR was modulated by providing a mental task when participants gazed at a fixation point and were exposed to unpredictable body motion induced by a motion platform; the modulation was described by changes in the parameter of the VOR computational model. However, further studies are required to determine whether these results are valid when participants actively operate a vehicle in an outdoor environment, such as driving. Therefore, in the present study, an experiment was conducted to investigate the influence of a mental task for car drivers based on a parameter of the VOR model. The MWL during the experimental trials was evaluated via the weighted workload (WWL) score calculated from the NASA-Task Load Index. Specifically, the eye and head movements were measured when participants drove a car in situations with and without a mental task. The experimental results indicated that the self-motion estimation parameter of the model decreased, and the WWL score increased with the addition of a mental task. The results suggest that the influence of MWL on VOR during driving can be described by the self-motion estimation parameter of the model.

Understanding the relationship between driving behavior and visual information is an important issue in order to understand driving behavior holistically. In this study, we constructed a driver model that reproduces the driver's steering behavior from visual information based on the Convolutional Neural Network (CNN) with human physical characteristics. We obtained the driving behavior in a simulator study to train the proposed CNN model. Which region in the visual field influencing drivers' steering behavior was analyzed using the results of the feature maps generated by the trained CNN model and the driver's gaze behavior. The results indicate that the drivers perform steering action using the information within 20 degrees from the gaze point.

A concern has been raised regarding the possible increase of motion sickness in automated vehicles. Therefore, automated vehicles without motion sickness must be developed to provide a comfortable space for drivers. In this study, we propose a control method of automated vehicles that reduces the incidence of motion sickness in passengers. In the proposed method, the desired trajectory for the lane-change task and the steering-controller improvements in a path-following controller to track the path are determined to minimize motion sickness, based on a computational model of motion sickness. Numerical simulation results demonstrate that the vehicle can be successfully controlled during the lane-change task. The effectiveness of the proposed method is demonstrated by comparing with comfort indices of previous studies.

Interest for motion sickness is increasing with increasing opportunities to view digital devices in transportation systems, including automated vehicles. Hence, technology for predicting and estimating motion sickness is essential. As one such technology, computational models for estimating motion sickness from head movements have been proposed and are used for motion sickness evaluation. However, a model capable of handling the effects of visual input and the visual-vestibular interaction for motions with six-degrees-of-freedom has yet to be developed. In this study, therefore, a computational model of motion sickness with visual-vestibular inputs is proposed by extending a model of the subjective vertical conflict theory of motion sickness for a vestibular input only. The proposed model inputs are the acceleration and angular velocity of the head and the visual perception of the angular velocity. A simulation conducted by inputting 1 h of sinusoidal oscillations demonstrated that the results are consistent with those of similar experiments conducted with human participants. In addition, the results of a simulation experiment conducted by changing the visual input demonstrated that motion sickness increases significantly when a conflict occurs between the visual and vestibular signals, which imitates the reading of a book in a moving car. Furthermore, we developed a method to calculate the predicted MSI using experimental data measured by IMU and camera image by approximating the visual perception of the angular velocity through an optical flow analysis. The results of inputting camera images and inertial measurement unit signals obtained by sine-wave-like pitching motion into the proposed model demonstrate that the proposed method, can describe the difference in motion sickness by the changes in the visual environment.

In a partially automated driving system, the driver is expected to respond to a request to intervene from the system in an emergency situation or functional limit. Currently, with regard to the transfer of driving authority, significant research has been performed on how to address the request that occurs when a functional limit is approaching. However, generating the request immediately before the limit may not allow the driver to operate in time. Consequently, a deterioration in vehicle stability will occur. Therefore, it is necessary to address the situations before reaching the functional limit named “uncertainty situation”. In this situation, the system should inform the driver without decreasing the driver's trust on the automated driving system. This is because the driver may not be convinced of the system if the request occurs many times without the functional limit. Therefore, in this research, we propose to introduce a shared authority mode that reduces the driver's workload under uncertain situations because of the partial responsibility for the driving operation. Experimental results from using a driving simulator indicated that the proposed method reduced the reaction time from the time a request was generated to the time of driver takeover operation, thus improving vehicle stability and reducing the workload.

While automated driving systems (ADSs), which correspond to level-two automated driving of Society of Automotive Engineers (SAE), are being developed actively, the ADS that learns the preference of drivers at an opportune time is required. In addition, there is a lack of assessments on ADS comfort. In this study, we focused on collision avoidance and developed an ADS that learns driver preference by driver intervention. By applying the gain-tuning and learning methods for gradual learning, the ADS can stably modify its behavior at an opportune time. The results of a driving simulator experiment indicated that driver comfort improved by using the proposed ADS. In addition, we confirmed that the ADS can guarantee usability during authority transfer in the learning process.

In controlling an authority transfer from automated driving to manual driving, there is a danger that an abrupt transition of steering control can cause disruptions to the handling. Furthermore, while paying attention to the run of the curve, the control transition becomes complicated. This is because the torque by the automated driving system is mixed with the torque for correcting the inclination of the vehicle. At such times, deleting the system torque so as to destroy the torque balance will cause the steering control to fall into instability. Therefore, in this research, we propose a control transfer method based on a shared mode during curve travel. As a result, the steering stability and vehicle stability were improved.

The vestibulo-ocular reflex (VOR), which is a reflexive eye movement generated in the opposite direction of head movement, is affected by volition of the head movement; it indicates the activeness or passiveness of the head movement. Research has yet to reveal the effect of actively operating or passively riding on a vehicle on a human's VOR. Therefore, in this study, we investigate the influence of the presence / absence of the operation of such a mechanical system moving in the pitch direction, as well as the frequency of operation, on VOR gain. Twelve subjects without disease in the vestibular system participated in the experiment. Experimental factors were the volition of operation, which includes active and passive, and the frequency of the operation, which includes 0.5, 1.0, and 1.5 Hz. They were set as the within-subject design. Subjects riding a vehicle are instructed to fixate on a target while the vehicle reciprocates in the pitch direction, as controlled by a joystick. The angular velocity of the eye, as well as the angular velocity and acceleration of the head and of the vehicle are measured. The experimental results show that VOR gain significantly increases in the active condition for operating frequencies of 0.5 Hz and 1.0 Hz. The measured data are analyzed using a computational VOR model previously proposed by the authors; these results suggest that differences in the volition of vehicle operation appear in the model parameters, which represent the accuracy of self-motion perception.

Vibrotactile display has been investigated to support driver's monitoring of traffic situation when auto driving. A vibrotactile display was assumed to contribute to driver trust in automation since driver can know the spatial awareness of an automated system to predict or understand the action selection for driving. The display provided the spatial information of close traffic objects with haptic stimulus. The present study considered driving scenes of passing a motorbike when vehicles are approaching from behind. A driving simulator study was conducted to investigate effects of the spatial information and driving behavior of automated system on driver trust. The results showed that the trust was affected by the information and behavior of the system.

Many mathematical models have been proposed to represent the Vestibulo-Ocular Reflex (VOR) that occurs during human head movement. It is known that the accuracy of these conventional models decreases when humans take on additional mental tasks. On the other hand, the 6DOF-SVC model, a mathematical model of motion sickness caused by the vestibular sensory system, includes a sub-model block representing the prediction accuracy of their own body movement, which is thought to be affected by mental tasks. Therefore, we aimed to describe the influence of mental tasks on VOR, and proposed a new mathematical model of VOR that utilizes estimated body movement calculated by the 6DOF-SVC model for calculating VOR. First, simulation experiments that compared human sensation in the horizontal direction during off-centered rotation based on measurements obtained from subjects, demonstrated that the direction of the gravito-inertial acceleration (GIA) estimated by the proposed model was in accordance with its human sensation in the steady state. Second, a model evaluation experiment was conducted to compare the measured VOR with the estimated VOR. The result demonstrated that the accuracy of the proposed model is as high as that of the conventional model. This suggests that the proposed model successfully includes the effect of estimation of body movement without decreasing the accuracy of the model compared to the conventional model.

As defined by the Society of Automotive Engineers (SAE), drivers are required to resume control from an automated driving system (ADS) in automated Level 2 and 3 driving when requested by the automated system. For example, the driver must resume manual driving control when the functional limitations of the ADS are approaching or have reached. In addition, he or she may also resume manual driving control whenever he/she realizes the functional limits of the system are approaching, or when an ADS malfunction is imminent. In such cases, it is necessary to smoothly transfer driving control to the driver, even when immediate steering actions are needed. Accordingly, this research proposes a method for smoothly transferring authority of steering control from the ADS to the driver in situations where drivers are required to perform quick steering actions. The method is based on a haptic shared control technique that can adjust the strength of the steering control by the ADS. We applied our method to a small electric vehicle and conducted experiments. It was demonstrated that the driver's intention to take over the control could be detected quickly only from the driver's steering operation. It was also suggested that aggressive steering behavior was suppressed with the proposed gain tuning technique, even in cases where the driver was required to engage in engage in immediate steering actions after resuming control.

Merging into the traffic flow on an expressway is a very challenging driving task. It is important to understand driver merging behaviors for building an effective driver assistance system. The first and most important task a driver performs when merging is decision making of the merging space in the oncoming traffic on an expressway. In this study, we aim to build a mathematical model to represent the decision making of the merging space of different drivers. The proposed model is based on the logistic regression model. Further, data for the proposed model is obtained from simulation experiments. We clarified that some explanatory variables were important for the proposed model by applying stepwise method for selecting the explanatory variables. High effectiveness in predicting the merging space by the proposed model was verified by the prediction accuracy of the model. Further, individual differences in merging behaviors among drivers are considered in this model. Therefore, it is implied that a driver's skill can be inferred by the investigation into the merging behavior of each driver by the model.

Haptic shared control is expected to achieve a smooth collaboration between humans and automated systems, because haptics facilitate mutual communication. A methodology for sharing a given task is important to achieve effective shared control. Therefore, the appropriate cooperative relationship between a human operator and automated system should be considered. This paper proposes a methodology to evaluate the cooperative status between the operator and the automated system in the haptic shared control of a steering operation using a pseudo-power pair of torque from each agent and the vehicle lateral velocity as each agent's contribution to vehicle motion. This method allows us to estimate cooperative status based on two axes: the initiative holder and the intent consistency between the two agents. A control method for a lane-keeping assist system (LKAS) that enables drivers to change lanes smoothly is proposed based on the estimated cooperative status. A gain-tuning control method based on the estimated cooperative status is proposed to decrease the assistance system's pseudo-power when intent inconsistency occurs. A method for switching the followed lane to match the driver's and assistance system's intentions is also proposed. A user study using a driving simulator is conducted to demonstrate the effectiveness of the proposed methods. The results demonstrate that the proposed methods facilitate smooth driver-initiated lane changes without significantly affecting the driver's torque or steering wheel angle while significantly improve lane-keeping performance.

The goal of this study was to develop a transfemoral prosthesis that realizes a natural and smooth gait. To realize such prostheses, the smoothness or goodness of walking in the swing phase should be investigated. So far, many studies have investigated the human gait. However, the goodness of walking has not been clearly defined. Passive walking has drawn much attention because it includes the essence of the gait with higyly energy-efficient walking. The human gait is thought to effectively utilize the dynamics of the body for a smooth gait. Investigating the human gait should help in building smoothly walking robots and realizing prostheses that make it easy to walk. The present paper proposes a method to evaluate the inertial motion of the human gait in the swing phase and a method to generate the joint motion profile of a transfemoral prosthesis that utilizes its inertial motion. First, an inertial motion index is proposed to evaluate the closeness of the given motion to the inertial motion of multilink systems. The gait of healthy participants in the swing phase was analyzed using the proposed index. The results showed that inertial motion is effectively used in the middle of the swing phase. Based on the results, a motion generation method is proposed for the knee joint of a prosthesis that utilizes the inertial motion in the middle of the swing phase. Simulation results with the method showed that a smoother change in torque was realized compared to another method that uses the measured joint motion of human gait as the desired trajectory for the PD controller.

It is expected that a collision avoidance system based on steering control could help avoid collisions even in cases where a collision cannot be avoided by braking only. For realizing steering-based collision avoidance, accurate environmental recognition and rapid motion planning in a highly dynamic environment are needed. The velocity potential field control method, which was proposed for mobile robot motion planning, has the advantages of a very low computation cost, even in dynamic environments with moving obstacles without any deadlock. In addition, the method generates the desired velocity vector to be followed but vehicle trajectory. With this feature, avoidance velocity vector is generated in realtime even in the case that the obstacles are moving and their movements cannot be anticipated. Thus, the present study proposes a collision avoidance method with steering control by generating a trajectory for obstacle avoidance using local environmental recognition based on application of the velocity potential field approach. In addition, a parameter determination method for the velocity potential function is derived. A preview steering control method for following the derived velocity vector is proposed. The simulation results obtained using vehicle dynamics software demonstrate that the proposed method can generate appropriate obstacle avoidance trajectories, even for moving obstacles, and the derived velocity vector can be tracked by an automobile.

We propose the asynchronous receiver-initiated MAC protocol with the stair-like sleep that each node reduces its own sleep time by the sleep-change-rate depending on the number of hops from the source to the sink in wireless sensor networks (WSNs). Using the stair-like sleep, our protocol achieves the high delivery ratio, the low packet delay, and the high energy efficiency due to the reduction of the idle listening time. Our protocol can formulate the upper bound of the idle listening time because of the feature that the sleep time decreases in the geometric progression, and the reduction of the idle listening time is obtained by using the stair-like sleep. In our proposed scheme, the sink calculates the sleep change rate based on the number of hops from the source to the sink. By using the control packets which have the role of ACK, our proposed protocol can achieve the stair-like sleep by no additional control packets. In addition, even in the network condition that multi-targets are detected, and hops to the sink are changed frequently, our proposed protocol can change the sleep change rate adaptively because the sink can always obtain the number of hops from the source to the sink. Simulation results show that the proposed protocol can improve the performances in the packet delivery ratio, the packet delay, and the energy efficiency compared to the conventional receiver-initiated MAC (RI-MAC) protocol.

Expert drivers' deceleration patterns in last-second braking will be formulated using a perceptual risk index for the approach and the proximity of a preceding vehicle as examples of comfortable braking patterns. It will be shown that the formulated braking pattern can uniformly generate a smooth deceleration profile for many approach conditions. In addition, the brake initiation timing of expert drivers will be successfully formulated using a modified index. Finally, an automatic braking system for collision avoidance will be proposed based on a formulated brake-initiation model and a deceleration pattern. Twenty five expert drivers will experience the automatic braking that is installed in an experimental car. It will be shown that the proposed system can generate a smooth profile and realize secure brake patterns based on the drivers' subjective evaluation.

To reduce rear-end crash of automobiles, it is important to judge necessity of deceleration assistance as earlier as possible and initiate the assistance naturally. On the other hand, we have derived a mathematical model of driver's perceptual risk of proximity in car following situation and successfully derived driver deceleration model to describe deceleration patterns and brake initiation timing of expert driver. In this research, an automatic braking system for collision avoidance will be proposed based on the formulated brake profile model and brake initiation model of expert driver to realize smooth, secure brake assistance naturally. It will be shown that the proposed control method can generate smooth profile for various conditions. In addition, experimental results using a driving simulator will show validity of the proposed system based on subjective evaluation.

Choice of area averaging strategy to produce data of normalized difference vegetation index (NDVI) is one of the sources of uncertainty, which is eventually propagated into the simulation results of global climate changes. The purpose of this paper is to investigate this scaling effects of NDVI, especially, on its monotonicity as a function of spatial resolution defined as a number of sampling points within a target area. A simple two-endmember linear mixture model (LMM) is used to analyze the differences among the results of area averaged NDVI with different resolutions. It is proofed analytically that the area averaged NDVI changes monotonically as the spatial resolution becomes higher (more number of sampling points within a fixed area) under the two-endmember LMM, only if a certain condition is satisfied. On the other hand, when the condition is not satisfied, the monotonic behavior of the averaged NDVI value is no longer guaranteed even within an assumption of two-endmember LMM.

In recent years, people have concern for their health. Especially elderly people have such tendency in the aging society. Appropriate exercises are important to maintain their health. In this paper, we develop the virtual reality snowboard system for whole body exercise and rehabilitation. Users can continue exercises pleasantly since the virtual reality snowboard system has amusement. In addition, we propose a methodology to provide the appropriate exercise corresponding to the personal condition by using the proposed virtual snowboard system. In the training, the longitudinal and lateral distances of sets of the flags and torque exerted on the board are controlled based on the user's conditions. Finally, we would show the effectiveness of our proposed method by measuring the heart rates, oxygen uptake and EMG signal of the lower limbs. It is shown that the exercise strength is enough for the training and the strength can be controlled by changing the positions of flags and torque by measuring the heart rates, oxygen uptake and EMG signals of lower limbs while playing snowboard.

A new method for the rehabilitation by playing virtual tennis is proposed. As the first step, we investigate whether the tennis swings are applicable for the range of joint motion exercise and the muscle strengthening exercise in this paper. For the evaluation of the range of joint motion exercise, we measure the motion range of the shoulder joints in various tennis swings. We measure EMG signals in various swings in terms of the swing velocity, the swing pattern, and the load exerted on the player's wrist in. order to examine applicability of the tennis swing to the muscle strengthening exercise. The resultant muscle activation will be compared with the usual rehabilitation exercise. From experimental results, we will show the tennis can be applicable to the rehabilitation training.

Robust manipulation strategies of deformable objects is presented. Manipulation of deformable objects can be found in many fields such as the garment industry and food industry. Guidance of multiple points on a deformable object is a primitive operation in the manipulation of deformable objects. In this guidance, the points often cannot be manipulated directly. A model of the manipulated deformable object is needed in order to perform these operations. It is, however, difficult to build a precise model of a deformable object. Thus, we need a robust control scheme that allows us to realize the operations successfully despite discrepancy between a manipulated deformable object and its model. We firstly derive a mathematical model of deformable objects for their manipulation. Second, indirect simultaneous positioning operations of deformable objects are formulated. Then, we propose a PID feedback control law with the rough object model to realize the manipulation. Furthermore, we propose a simple PID feedback control law without deformation model. The validity and the robustness of the proposed manipulation method is shown through simulation results.

A systematic approach to the modeling of deformable rodlike objects is presented. Various rodlike objects such as cords and wires are manipulated in many manufacturing processes. In such processes, it is important for successful manipulation to evaluate their shapes on a computer in advance because their shapes can be changed easily and their deformation often shows hysteresis properties. In this paper, we develop an analytical method to model the shape of deformable rodlike objects including hysteresis properties. First, we investigate the mechanism of hysteresis. Second, the potential energy of a rodlike object and the geometric constraints imposed on it are formulated. The shape of the object can be derived by minimizing the potential energy under the geometric constraints. Thirdly, a procedure to compute the shape of a deformed rodlike object is developed by applying a non-linear programming technique. Finally, we show some numerical examples with hysteresis using our proposed method.

A control method for positioning operations of deformable objects is presented. In many manipulative operations of deformable objects, it is required to guide multiple points on on object simultaneously. Moreover, the points often cannot be manipulated directly. A model of the manipulated deformable object is essential to perform these operations. It is, however, difficult to build a precise model of a deformable object. Thus, we need a new control scheme that allows us to perform the operations successfully despite of discrepancy between a manipulated deformable object and its model. We introduce a coarse model of a deformable object and develop a control law for its positioning operation. First, we propose a mathematical model of deformable objects for their positioning operations. Second, indirect simultaneous positioning operations of deformable objects is formulated. Then, we propose an iterative control method to realize a given positioning operation. The validity of the proposed method and the effect of model errors on the operation are examined through experiments using textile fabrics. Experimental results show that coarse model of deformable objects is effective for their positioning operations. Finally, we discuss the locations of robotic fingers, which apply forces to a manipulated object.

A novel control method for the indirect simultaneous positioning operation of soft deformable objects will be proposed. In some operations, multiple points on a deformable object should be positioned to desired regions simultaneously. In addition, these positioned points often cannot be operated directly and we have to perform these operations indirectly by controlling other points. We call these operations indirect simultaneous positioning operations. First, a simplified physical model of deformable textile fabrics is developed for their positioning operations. Second, the indirect simultaneous positioning operations are formulated using the proposed model. Based on the linearized model of the deformable objects, we will propose a novel control method for the indirect simultaneous positioning operations with visual sensors. In this method, positioned points can be guided to the desired locations by controlling the positions of operation points. It should be noted that our control method works well even if exact physical properties of fabrics cannot be given. Experimental results will show the validity of our proposed method. In addition, a condition to examine whether a given operation is feasible or not will be considered based on the linearized model.

A new approach to the modeling of plain knitted fabrics is presented for their deformation control. There exist many manipulative operations that deal with deformable soft objects such as clothes, papers, wires etc. At present, most of these operations are still done manually by skilled workers. Therefore, it is important to automate this process, especially the process of knitted fabrics manipulation in the garment industries. However, automatic handling of knitted fabrics is difficult to perform by machines since the knitted fabrics are often deformed and extended during the operations. Furthermore, it is difficult to control the shape of the knitted fabrics. In order to control the deformation of knitted fabrics, a physical model of the fabrics is essential. In this paper, we present a physical model of plain knitted fabrics. Firstly, the structure of plain knitted fabrics is explained. Secondly, the behavior of cross points of a plain knitted fabric is investigated experimentally. Then, a physical model of the fabrics is developed based on the iterative structure of the fabrics. Model parameters are selected considering the behavior of the cross points. Finally, the validity of the proposed model is examined by comparing computed shape of a fabric with its real image.

We explore possibility of virtual tennis in this paper. To realize virtual tennis, it is necessary to provide our hands with reaction forces from a tennis ball. Moreover, we need a head mounted display which is capable of showing moving objects with high speed. In this paper, we develop an equipment which makes reaction forces at a grip of a tennis racket. In the developed equipment, several wires are radially stretched from the grip in order to produce force and torque vectors with six degrees of freedom. The motion area of the proposed system is made clear at first. A novel method to determine wire tensions which is suitable for real time calculation is proposed. Finally, a basic experimental result is demonstrated.