To evaluate unsafe driving, it is essential to identify the driving behavior characteristics of skilled drivers in identical situations as a standard for safe driving.Previous studies on skilled drivers have reported individual differences in behavior depending on specific conditions or indices.These variations pose challenges in understanding driving behavior characteristics in complex driving scenarios.In this study, we focus on probabilistic decision-making theory and propose a method to extract the positional commonality of skilled drivers' driving behavior selection in scenarios requiring multiple driving behaviors.Utilizing data collected through self-localization technology, which accurately determines the vehicle's position, we analyzed the driving behavior selection characteristics of skilled drivers in complex driving situations.The results revealed that the driving behavior of skilled drivers in complex scenarios varies across three distinct zones.The extracted characteristics functioned as a safe driving index in evaluating unsafe driving, confirming the validity of the proposed method.

Safety assurance tests reflecting actual accidents or near-misses are necessary to ensure the safety of automated driving systems.In such tests in a simulated environment, vehicle trajectories and road environments reproduced from nearmiss data recorded on a drive recorder are used.In this study, a method is proposed to reconstruct the trajectory of the ego vehicle and the road environment at the time the vehicle traveled from the dashcam video recorded on the drive recorder.The movement of the ego vehicle is obtained using image registration utilizing weights calculated from the semantic information obtained by semantic segmentation.Based on the ego vehicle's motion between frames calculated by image registration, the trajectory of the ego vehicle and the map of the driving environment are reconstructed.The validation of the proposed method on several dashcam videos suggests that it is possible to reconstruct the driving environment by image registration using semantic information.

Traffic accidents on community roads, which frequently have intersections with poor visibility, are one of the social issues in Japan. Although safety technologies that utilize roadside sensors are expected to be effective for Japanese community roads, only a few roadside sensors and limited sensor coverage are available on community roads in practice. In such an environment, it is difficult to consistently track multiple traffic participants by associating sensor observations of them from one sensor coverage to another coverage. To address this difficulty, we propose a data association method for multi-target tracking on the assumption that targets can be outside the sensor coverage. The proposed method calculates the existence probability of each target being within the sensor coverage at each time step and incorporates it as a prior probability in the data association process. In the simulation experiments, comparisons with existing methods demonstrate that the proposed method achieves a higher association success rate in various conditions. Furthermore, real-world experiments validate the feasibility of the proposed method.

This study has developed a novel method to estimate the singular stress field at the bonded edge by applying an inverse analysis on the deformed shape of the adherend, without explicitly considering microscopic behaviors of the bonded interface.The mathematical model used in the inverse analysis does not need to be a model of the entire adherend, and can be a partial model of the bonded member based on the free body diagram concept.In the inverse analysis, the Tikhonov regularization is applied to reduce the influence of displacement measurement errors on the estimated values.In order to verify the basic validity of the estimation method, peel tests were performed in numerical simulations and actual tests, and the singular stress field at the bonded edge was estimated by the estimation method.In the verification, the regularization parameter selection in the inverse analysis was discussed based on the test results.The numerical simulations show that both the stress intensity factor and the stress singularity index remained almost constant between a certain range of the regularization parameters, and were close to the correct values.Then, the inverse analysis results agree with the correct values.The actual test results show good agreement with each other, and are close to the direct analysis results.These results indicate that the proposed estimation method is effective for estimating the singular stress field at the bonded edge.

This study addresses an issue arising during requests to intervene, usually focused on the direct shift of control from the system to an unprepared driver. We explore an approach based on the concept of adaptive automation. The approach is utilized to establish a strategy that the automated driving system can modulate between different driving states (Level 0 (L0) to Level 3 (L3) driving automation) based on traffic situations. Furthermore, we aim to design Human-Machine Interface (HMI) instructions that align with two design perspectives: system-centered (Implicit) and human-centered (Explicit). These HMI instructions critical to the strategy implemented are intended to sustain awareness on driver roles and decrease inappropriate driver responses during transitions. In the experiment, we utilized two approaches (adaptive control transition strategy and fixed control transition strategy) and two HMI designs. The experiment with a driving simulator was conducted with 60 drivers. The research questions explored include the influence of HMI design on drivers’ awareness during shifts from L3 to L2/L1. Also, we examine how different combinations of a strategy and HMI design affect reaction times from L3 to L0 scenarios. Compared to Implicit-HMI, the use of Explicit-HMI did not lead to inappropriate actions associated with their driving roles. This indicates that Explicit-HMI facilitates accurate response to state changes during the implementation of the adaptive approach. In terms of the comparison of the combinations, it is found that the adaptive approach provides initial guidance for drivers to monitor the environment, thereby providing earlier reaction time for the transition to manual.

In an effort to improve automated driving and advanced driver assistance systems (AD/ADAS) performance, the industry is gradually moving toward greater vehicle autonomy. In this context, particular emphasis is being placed on the use of in-vehicle sensors and cameras. While these systems are primarily intended to replace or support the driver with invehicle devices, the ability to actively perceive non-visible areas can significantly improve vehicle safety and optimize route decisions. Traditional navigation maps (SD-MAP) have served their purpose, but often lack the necessary dynamism and accuracy; Vehicle-to-Everything (V2X) technology, particularly in the area of Vehicle-to-Network (V2N), is a growing area of interest due to the proliferation of smart phones and offer promising alternatives that leverage the strength of cellular networks. This study will evaluate the utility of smartphones as a central element of vehicle sensing and investigate their potential to provide critical lane-level recognition data for next-generation AD/ADAS applications. Through a comprehensive system architecture, smartphone GNSS data is processed to estimate lane-specific traffic conditions. In addition, current limitations and potential enhancements with respect to the use of OpenStreetMap (OSM) data will be detailed. While the accuracy of lane identification based solely on data from smartphones remains a challenge, this paper explores possible countermeasures, highlighting the integration of various data sources to achieve a holistic vehicle control system.

Automated driving safety evaluation predominantly relies on scenario-based approaches. In this study, the authors adopt a functional scenario catalogue initially conceived by JAMA to evaluate automated driving safety on limited access highways. The potential of this catalogue to cover real-world crashes was investigated by comparing each scenario in the catalogue with crash patterns from two international data sources: the 2007 NHTSA’s pre-crash scenario typology for crash avoidance research report, and the 2020 IGLAD’s codebook. The results indicate the potential of the scenario catalogue to comprehensively cover both the NHTSA and the IGLAD crash scenario typologies.

ABSTRACT: The nozzle hole deposit formation is investigated by the test rig in which deposit formation can be accelerated at various injection pressures, injection durations, and nozzle tip temperatures. The rotational speeds of the fuel supply pump are varied to obtain the different times between injections. The Bosch method and the constant volume chamber with a high speed video camera are used to investigate the effects of deposit in the nozzle hole on injection rate and spray characteristics. The results show the longer time between injections promotes the nozzle hole deposit and affects both the fuel injection and the spray characteristics.

Indoor temperature affects the arousal level and thermal comfort of drivers. In this study, we aim to prevent the drowsy state and to improve the thermal comfort of drivers in driving for a long time using changes in ambient temperature in cabin. Targeting the scene of driving for 90 minutes, the hypothesis that repeated thermal stimulation to the driver can prevent the decrease of arousal level and thermal comfort of the driver was tested. We investigated the drowsiness level, thermal comfort based on subjective evaluation, and physiological parameters of the driver in the condition of repeated thermal stimulation for 90 min with driving in a simulator. As a result, the drowsiness level with a scale from 0 (alert) to 4 (extremely drowsy) was kept below 1 (slightly drowsy), and the comfort sensation with a scale from -3 (very uncomfortable) to 0 (comfortable) was maintained above -1 (slightly uncomfortable). These results suggest that repeated thermal stimulation is valid to prevent the decrease of arousal level and thermal comfort of the driver.

As concerns grow over the security of the Controller Area Network (CAN) bus used in modern cars, researchers are developing technologies to mitigate potential cyberattacks on it. Some of those technologies rely on CAN messages’ arrival timestamp (i.e., the point of time when a receiving node first observes the CAN frame) to measure slight differences between the clock references of Electronic Control Units (ECUs). Those technologies are promising, but several unknown factors still limit their efficiency and accuracy. Therefore, we investigated the properties of arrival timestamps of CAN messages and studied how network properties can impact them. First, we established a mathematical model for the case of two separate ECUs sending a message each. Then, we verified that model with a network simulator that can simulate an arbitrary number of ECUs sending arbitrary messages with different clock references. Finally, we verified those results on actual hardware and observed arrival timestamps for various scenarios. We concluded that the standard deviation of the arrival timestamp of periodic messages is a dynamic source of information about the physical network, rather than a static property of the sending ECU, as usually modeled. Those results can help researchers improve technologies that rely on CAN messages’ arrival timestamps.