Standing passengers in public buses face increased fall risk during sudden braking and acceleration, yet they often remain standing despite available seats due to proxemic discomfort, seating norms, and trip goals. We present a two-day, in-the-wild deployment of a minimal social robot “conductor” that delivered real-time, context-aware greetings and seating/safety prompts on regular bus routes, driven by an AI-based electronic control unit (AI-ECU) pipeline that recognized passenger state and seat-zone availability (front, priority, rear) from onboard cameras. Using synchronized video and system logs, we analyzed 74 compliance-potential episodes among 670 boardings. Overall seat-taking compliance was 27.0% (20/74) and tended to be higher when passengers visibly attended to the robot. When passengers complied, they were more likely to choose rear seating, suggesting that compliance depended on the emergence of a socially unambiguous, low-friction option rather than availability alone. The findings show that robotic safety prompts are negotiated through situational constraints and normative seating logics, motivating designs that make socially acceptable seating options salient, low-friction, and well-timed in shared public environments.

To mitigate the occurrence of traffic accidents, addressing the human factors pertinent to road safety, particularly driver operational errors, is essential. Drivers in urban environments can benefit from readiness evaluations and feedback, which help mitigate errors, promote safer driving speeds, and enhance situational awareness. This study aimed to develop and validate a driver behavior assessment system centered around the concept of stepwise driver readiness. The system is designed to evaluate driver behavior and provide progressive feedback to improve performance. We extrapolated the original concept of driver readiness to create a stepwise readiness evaluation system, which was implemented and tested on urban roads to evaluate its effectiveness. The results demonstrate that the system effectively evaluates readiness, and the stepwise feedback mechanism significantly enhances driver performance. Notably, the success of the feedback process was influenced by the level of driver acceptance. These results highlight the importance of the expanded driver readiness concept in managing human factor-related driving risks and improving road safety.

Geospatial machine learning is of growing importance in various global remote-sensing applications, particularly in the realm of vegetation monitoring. However, acquiring accurate ground truth data for geospatial tasks remains a significant challenge, often entailing considerable time and effort. Foundation models, emphasizing pre-training on large-scale data and fine-tuning, show promise but face limitations when applied to geospatial data due to domain differences. Our paper introduces a novel image translation method, combining geospatial-specific pre-training with training and test-time data augmentation. In a case study involving the translation of normalized difference vegetation index (NDVI) values from synthetic aperture radar (SAR) images of cabbage farms, our approach outperformed competitors by 31% in a public competition. It also exceeded the average of the top five teams by 44%. We publish both our image translation method with baseline methods and the geospatial-specific dataset at https://github.com/IBM/SAR2NDVI.

A highly anthropomorphic Robotic Human Machine Interface (RHMI) integrated into car dashboards has shown effectiveness in promoting safe driving behaviors, as it is accepted as a driving agent. However, which anthropomorphic elements in the RHMI’s appearance are essential for achieving driver acceptance remains unclear. Identifying these elements could facilitate the development of a minimal design and reduced installation costs for RHMIs on car dashboards. In this study, we conducted an online video experiment to explore the impact of RHMI embodiment and anthropomorphism levels on user acceptance. The findings provide insights for designing cost-effective and minimalist RHMIs as driving agents.

Soon, autonomous cars are expected to become widespread, but at present, it is still common for people to drive cars manually. A driving support agent (DSA) is used to support driving using the human-agent interaction approach. In previous research, it was shown that DSAs can be useful in assisting the user through voice utterances. However, previous studies focusing on DSA utterance design have not compared off-record strategies with other politeness strategies that explicitly communicate the speaker's intentions, and it was not clear whether DSA should provide explicit utterances to the user. Therefore, in this study, a video-based subjective evaluation experiment (n=240) was conducted to compare the acceptability of off-record strategies, positive politeness strategies, negative politeness strategies, and direct utterances without politeness strategies. The results of the experiment showed that the negative politeness strategy was evaluated significantly higher than the off-record strategy on evaluation items related to functionality. This result suggests the usefulness of providing explicit instructions with linguistic consideration (politeness) when DSA provides driving assistance to users. In addition to the above findings, there were several correlations between the user's personality characteristics and the subjective evaluation of the DSA. Specifically, there was a significant positive correlation between the level of conscientiousness of the user and the direct utterance evaluation value. For the positive politeness strategy and off-record strategy, there were no significant correlations between evaluation categories of the dislikeability and users' personality characteristics. These results suggest that individual differences in n egative impressions due to personality characteristics are small in the positive politeness and off-record strategies.

Automated driving systems are envisioned as the future mode of transportation owing to their projected ability to reduce human error and achieve more efficient and comfortable transportation. Accordingly, designing an interface that ensures the situational awareness of the human operator to reduce confusion, false expectations, and over-reliance on the automated system is important. When a human operator is in control, the automated system is expected to handle troublesome situations that the human is unable to manage. Thus, an interface is required to provide the appropriate information when necessary so that the human operator can easily perceive the reason for the sudden automated intervention. In this study, such a scenario is highlighted, in which a simulated automated intervention avoided a potential collision with a pedestrian who suddenly appeared on the roadside. To convey the reason for the automated intervention, an augmented reality-based head-up display (AR-HUD) cue that targets the pedestrian is developed. To understand the effects of the AR-HUD cue on the speed at which a human operator can recognize a pedestrian and the contribution of this visual cue to the perception of acceptability and credibility of the automated intervention, we compared AR-HUD with a static head-up display (S-HUD) that displays a pedestrian symbol at the bottom portion of the windshield. The results showed that the AR-HUD cue yielded faster recognition of the targeted pedestrian and provided a relatively more acceptable perception of the automated intervention.

Japan has become a more aged society and there are more drivers, 65 years of age and above. Cars represent an important mode of transportation for the elderly; however, in recent years, the number of traffic accidents caused by elderly drivers has been on the rise, and this has become a social issue. Thus, to ensure driving safety, we study a driver agent system that provides driving and feedback support to the elderly drivers for encouraging them to improve their driving. In this paper, we present a summary of the proposed agent and report on a set of experiments using our agent for the elderly and non-elderly drivers in an actual environment with car on public roads. From the analysis of driving operations and fixation points during driving, the results revealed that the acceptability of the agent was high, the agent in the actual car environment did not distract the driver, and the agent could improve driving behavior.

Older drivers experience a high rate of crashes due to road intersections, unseen objects, and failure to find the traffic signals. These characteristics would be recognized if a system could watch and monitor the behaviors of the drivers inside their car. Therefore, in this study, current advances in driver monitoring cameras are used for measuring the head movements of older drivers to evaluate the visual intent of the driver. Several quantitative metrics that compute the temporal and spatial aspects of head movements allow for assessing the behaviors of older and middle-aged drivers. A driving simulator study using urban road scenarios shows that at high vehicle speed, on average, older drivers move their heads more slowly within a narrower range and glance too quickly to recognize surrounding traffic correctly. Correlation analysis validated the high prediction capabilities of head-movement measures towards the future evaluation of driving risks.

The purpose of this research is to analyze overconfidence and dependence caused by receiving the utterance from a driving support robot when a driver drives a car. Comfortable driving can be performed by receiving support from the driving support robot. However, it is expected that drivers will lead to accidents by the dependence or overconfidence bias on support robots. The dependence bias is that driver decreases the quality of driving behavior with respect to the function of the safety system, and the overconfidence bias is that driver embraces expectations more than the performance of the driving support robot. Although this problem includes the fuzziness of human characteristics, the conventional driving support system does not take into consideration such influence of the overconfidence or dependent bias. In order to verify whether driver has the bias against driving support robot in this paper, we investigated the impression of the robot when the driving support with driving animation and robot and report the results.

Cars represent an important mode of transportation for the elderly; however, in recent years, the number of traffic accidents caused by elderly drivers in Japan has increased. Thus, to ensure driving safety, we are researching a driver agent system that provides driving support and feedback support to elderly drivers to encourage them to improve their driving. In this paper, we report on a set of preliminary experiments using our agent in an actual car environment designed to evaluate the subjective acceptability of and distraction by the agent based on subjective evaluation and analysis of driver fixation points during driving. The results revealed that the acceptability of the agent was high and that the agent in an actual car environment did not distract the driver.

In recent years, the number of traffic accidents caused by elderly drivers in Japan has increased. Cars are an important mode of transportation for the elderly. Thus, to ensure their driving safety, a system that can assist elderly drivers is required. In this study, we propose a driver agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. In this paper, we describe the proposed driver agent system. We conducted an experiment to evaluate driving behavior improvement through three types of support: driving support, review support, and a combination of both. The results revealed that the combination of both supports lead to the greatest improvements in driving behavior and was most acceptable to elderly drivers.

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, a car is an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. In this study, we propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system based on the analysis of the teaching records of a human instructor, and the subjective evaluation of driving support to elderly driver from three different agent forms, a voice, visual, and robot. The result revealed the robot form is more noticeable, familiar, and acceptable to the elderly than other forms.

Frequent and uncontrolled interruptions by information systems that do not reflect the user’s state can result in fragmented working times and decreased intellectual productivity. To avoid adverse interruptions, interruptibility estimation methods based on PC operation information have been proposed. However, workers who use PCs to accomplish their primary tasks occasionally engage in paperwork. Occasional paperwork activities, which are not reflected in the PC’s operation information, can cause estimation errors. This study focuses on using the position of the head, posture, temporal motion, and continuity of the head position and posture while a worker is at his or her desk as indices to reflect engagement in the task at hand. Based on an analysis of the relationship between the head-related parameters and interruptibility, an interruptibility estimation algorithm is proposed using four head-related indices that reflect interruptibility during PC and non-PC work. Experiments indicate that estimation accuracy improves as a result of incorporating these indices in the algorithm.

The first international workshop on mood engineering (IWME) is held in Tsukuba, Japan on October 28, 2014 and co-located with the second international conference on human-agent interaction (HAI). The main objective of the workshop is to create artificial moods by robot and agents and to clarify the mechanism of a mood created by a group including humans, robo ts, and agents. We have constructed an exciting program of demonstrations, posters, and video materials as interactive sessions that will provide participants creative a mood for collaborative research.

Office workers are affected by surrounding environment including conversations and visitors. Interrupting a particular worker degrades not only his working efficiency but also the efficiency of others. Therefore, the estimation of interruptibility of entire office is required to attain appropriate interruptions. In this study, a feasibility of automatic estimation of office interruptibility based on the camera image and sound is discussed.

The mosquito proboscis is composed of seven micro parts. Among them, labrum and maxillae play important roles in insertion. The labrum is a central needle which sucks blood; two maxillae are left/right needles aside the labrum, respectively. They cooperatively move, i.e., one maxilla goes forward, labrum goes forward, another maxilla goes forward, successively. We noticed that the maxilla has a jagged harpoon-like shape on its tip area, which has an anchoring effect. We proposed the easy insertion mechanism as follows: the equivalent stiffness, which is the ratio of the resistance force relative to the moving distance, is considered. The total stiffness in pushing out the central needle can be made smaller than zero due to the anchoring effect of the side needles. We named it as “negative stiffness mechanism.” Equivalent stiffness during pulling/pushing a fabricated needle was experimentally estimated, which indicated the validity of proposed mechanism.

In this study, we propose a user-interruptibility estimation method based on focused application switching (AS) during PC work. It was experimentally demonstrated that the interruptions of AS are more acceptable than those during continuous work. Therefore, we constructed an algorithm that estimates the interruptibility of AS at three levels based on 19 selected indexes. The feasibility of the interruptibility estimation of AS was demonstrated by an estimation experiment using another dataset of 11 users.

This paper proposes a new leg-wheel hybrid jumping robot ldquoAirHopperrdquo. AirHopper is expected to overcome uneven terrain, which is difficult to move around for ordinary vehicles which carry supplies for disaster areas or isolated districts. To acquire high mobility and jumping, AirHopper consists of the following mechanisms. 1) 4 legs that are widely spread like a spider for stability, 2) active wheels on its feet which are driven by built-in motors and transmissions, 3) 2 decoupled pneumatic cylinders per leg and a built-in piston that drive 4-bar linkage leg mechanism. In this paper, we proposed the design of a powerful light weight leg-wheel mechanism, and verified its validity. AirHopper jumped 850 mm vertically, and landed on the ground softly by means of controlling in-cylinder pressure. Running jump motion was also achieved.

Learning systems such as multi-layer feed-forward neural networks, wavelet networks and so on need appropriate learning data (input data and teaching output data). These methods are not so useful in case when we cannot get the appropriate learning data. Even in this case, it is not so difficult to evaluate the system output for arbitrarily applied input. The learning data of input-output pairs with their evaluations are easily obtained and thus is easily used for modeling the system. SOR (self-organizing relationship) network is a modeling tool, which can be established by a set of input-output data and corresponding evaluation. This SOR network can act as a knowledge acquisition system and also act as a fuzzy inference engine. The linkage among the units in competitive layer is fixed and not flexible, and thus not used for complicated systems. In this plenary talk, the advanced learning process is presented for the original SOR network by employing evaluation-based TRN (topology representing network). By this learning, the linkage among the units in the competitive layer can be more flexible and thus used for modeling of much more complicated systems. The application of the SOR network established by this learning process to a manipulation control is also presented.

In order to search survivors in half-collapsed buildings at disastrous sites, the authors have developed the throw & collect rescue inspector. It is composed of a throwing and a drawing mechanisms and a child machine equipped with camera. The parent robot is inserted to the site and starts the inspection of the area. If an obstacle is in the way, it throws out a child machine with its magnetic brake pneumatic cylinder, and the inspection can be performed beyond the obstacle while the child machine is drawn back. This report shows the details of the structure of the robot.

This paper describes an overview of an online training support system for ADR mediators. To educate good mediators, much training is necessary, which is not easy for supervisors. As supervisors have to take care of many students, they cannot spare much time for a specific student. To train students effectively, some support system is needed.This system provides an environment for online disputation. Using this system, the supervisor and students can participate in the mediation process even if they are outside of the University. Furthermore, this system stores many disputation records in the form of XML documents as a case base, and this case base is used to navigate the mediation process. During the disputation, users can retrieve old similar scenes of disputation, and they can construct proper arguments by referring to similar scenes. Furthermore, by comparing records of disputation or by analyzing them statistically, we can get the information that help to evaluate the mediation skill.

This paper describes the numerical analysis and design for a higher jumping rescue robot using a pneumatic cylinder. First, the basic equations for jumping are derived and the simulation is performed. Then, the relationship between the jumping height and the pressure-receiving area of the cylinder is considered when the volume or the stroke of the cylinder is kept constant. This allows calculation of the optimal cross sectional area. In addition, the jumping height is also affected by the weight ratio between the rod and the cylinder tube. Based on these results, a robot equipped with a cylinder of the appropriate dimensions controlled by a well-selected valve is re-engineered and demonstrated. Experimental results show that the improved robot can jump considerably higher than the former design with the same energy efficiency, as shown in the following video. http://www.cm.ctrl.titech.ac.jp/study/jump/home.html.

This paper discusses the design and motion experiments of the newly constructed jumping & rolling inspector Leg-in-rotor-II. The features of Leg-in-rotor-II are as follow; (i) The driving method of 3-D jumping & rolling by the reduced degrees of freedom on the separated drive. (ii) The introduction of the passively stored leg. (iii) The introduction of the wheel with the light anisotropy elasticity of the high changing ratio. (iv) The pneumatic jumping control method and its energy saving, (v) The sensing method to estimate the desired jumping start point. (vi) The structure of the sideway tumble prevention and the shock buffer. Finally, the motion experiments of Leg-in-rotor-II to roll, jump and land on debris are shown and the validity of the introduced design methods is verified.

A moot court is an effective method to educate the skills of argumentation. However, to conduct the moot court, participants have to come together at one place and have to stay there for a long time. Therefore, it is difficult to hold the moot court so often.