Hiroshi Yoshitake

To effectively correct older drivers’ self-estimation, we proposed a review method that considers the attitude change associated with the correction of their overestimation. First, reanalyzing previous data, we hypothesized that the attitude change in optimistic comparison with same-age drivers is associated with the degree of overestimation correction. To verify this hypothesis, we conducted an attitude survey before and after a self-estimation correction education. The results supported this hypothesis. Based on these findings, to reduce their optimistic attitude toward their own driving compared to same-age drivers, we proposed a review method that presents and compares the driving evaluations of other same-age drivers alongside the participant's own driving evaluation in a driving simulator. To evaluate the proposed method's feasibility, we conducted a preliminary experiment with three older drivers, which indicated its potential to effectively reduce optimistic comparative attitude compared to same-age drivers and correct overestimation.

Public buses face challenges such as driver shortages and accidents, and automated buses are attracting attention as a solution. Ensuring safety is crucial, and operating at a predetermined safe speed is one approach. Currently, this speed is often set through trial and error, relying on manual driving data and driver experience, which lacks theoretical basis and is time-consuming. To address this, our study proposes a method to theoretically determine a safe speed that allows automated buses to avoid collisions, particularly at intersections with limited visibility. Two critical speeds are calculated: the maximum speed at which the bus can stop before reaching a pedestrian, and the minimum speed at which the bus can pass before a pedestrian enters the collision area. As the bus approaches the intersection and visibility improves, the required minimum passing speed decreases, and the two speeds may converge. This common speed is then defined as the lower limit of safe speed. To validate the method, numerical simulations were conducted to evaluate the effectiveness of the proposed speed calculation method.

Ensuring the safety of Automated Driving Systems (ADS) prior to their deployment on public roads is of paramount importance, particularly given the complexity and variability of real-world traffic environments. Effective safety assessment requires the careful selection of test scenarios that accurately reflect the system's operational design domain. For ADS operating along fixed routes—such as autonomous buses—vehicle behaviors and road environments can be analyzed in conjunction with traffic accident statistics to derive representative risk scenarios. However, such scenarios are often described in broad terms due to limitations in the available data, lacking the granularity needed for rigorous safety validation. To address this issue, it is essential to define specific scenario parameters that represent potential risk conditions. Although expert knowledge can aid in this process, it is inherently subjective and poses challenges for systematic prioritization of scenarios. To mitigate these limitations, this paper employs a near-miss incident database derived from human-driven vehicles, based on the premise that both ADS and human drivers navigate under similar real-world conditions. By analyzing patterns within this database, it becomes possible to identify scenario parameters objectively. This paper applied the proposed methodology to typical accident patterns, confirming the feasibility of identifying key scenario parameters associated with elevated safety risk. The results contribute to the development of more reliable and efficient scenario-based safety assessments for ADS.

There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both comfort and efficiency, even in crowded environments, is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient autonomous navigation method without increasing passenger discomfort, considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path-planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method has a potential to achieve efficient autonomous navigation in crowded environments.

Detecting occluded areas in a driving environment is crucial to preventing traffic accidents against hidden road agents coming out from such occluded areas. Our previous work proposed a novel detection method that can offer geometric information of the detected areas by utilizing camera and LiDAR sensor fusion. However, it had difficulty identifying individual areas formed by different objects without information about distinct objects. Thus, the objective of this study was to improve our previous methodology, and panoptic segmentation, which can distinguish between individual objects and offer semantic class labels of the object, was adopted to overcome the limitation. Evaluation results revealed that our proposed methodology could achieve satisfactory results in occlusion area detection and superior accuracy in estimating hidden road agent types in the detected areas.

Autonomous personal mobility vehicles (PMVs), such as electric wheelchairs, are meant to drive through pedestrian spaces. Cooperative pedestrian avoidance by PMVs is necessary in these spaces to maintain smooth traffic. Previous studies suggested that PMVs can avoid pedestrians on a shorter path without compromising each other’s acceptability. This avoidance can be realized by understanding how pedestrians react to the behavior of PMVs and considering those characteristics in the autonomous navigation of PMVs. In this study, the characteristics of pedestrian’s avoidance behavior were investigated. Experiments were conducted to understand the influence of the parameters of the PMV’s avoidance behavior on pedestrians. Results showed that the angular velocity of the PMV during avoidance affects the pedestrian’s avoidance width and tolerance against the PMV’s behavior. These results suggest that it is possible to avoid pedestrians in smaller avoidance spaces by controlling the angular velocity of the PMV and maintaining smooth traffic.

The mammalian oviductal lumen is a specialized chamber that provides an environment that strictly regulates fertilization and early embryogenesis, but the regulatory mechanisms to gametes and zygotes are unclear. We evaluated the oviductal regulation of early embryonic development using Ovgp1 (encoding an oviductal humoral factor, OVGP1)-knockout golden hamsters. The experimental results revealed the following: (1) female Ovgp1-knockout hamsters failed to produce litters; (2) in the oviducts of Ovgp1-knockout animals, fertilized eggs were sometimes identified, but their morphology showed abnormal features; (3) the number of implantations in the Ovgp1-knockout females was low; (4) even if implantations occurred, the embryos developed abnormally and eventually died; and (5) Ovgp1-knockout female ovaries transferred to wild-type females resulted in the production of Ovgp1-knockout egg-derived OVGP1-null litters, but the reverse experiment did not. These results suggest that OVGP1-mediated physiological events are crucial for reproductive process in vivo, from fertilization to early embryonic development. This animal model shows that the fate of the zygote is determined not only genetically, but also by the surrounding oviductal microenvironment.

The application of self-driving technology to personal mobility vehicles (PMVs) is being investigated. Passengers of self-driving PMVs will likely be engaged in activities with visual processing, such as operating smartphones. Thus, self-driving PMVs need to consider the possibility that passengers will perform these activities and the ease of performing these activities. However, little research has considered such passengers' convenience when using self-driving PMVs as a service. It is known that visual information is particularly dominant among input information to human beings. As was observed in previous works, changes in the surrounding environment due to moving may negatively impact the passenger's main activities with visual information processing. The traveling speed affects these changes in the surrounding, and it is a parameter of self-driving methods. Therefore, this paper aims to grasp the effect of traveling speed on passengers' visual processing during self-driving of PMVs to realize self-driving PMVs on which passengers can easily process information. The effect of the traveling speed of self-driving PMV on task performance and mental workload of passengers' visual processing was investigated by conducting a simulator experiment. The results showed that although the task performance is maintained, the mental workload of visual processing increases when the PMV moves compared to the stopping condition. Moreover, it was suggested that the visual input of the changing environment and the passenger's gaze behavior influence the increase in mental workload.