With the rapid advancements in automated driving technologies, there is a growing demand for the commercialization of advanced automated vehicles. Through these technologies, we envision enjoying various types of entertainment in automated vehicles, apart from manual driving. To achieve widespread acceptance of automated driving, appropriated interactions between users and automated driving systems must occur. From users’ perspective, automated driving vehicle must be operated within users’ comfort, safe, and satisfying perception based on their personal driving style such as aggressive and defensive driving. Thus, during the motion planning phase of automated driving, consideration should be given to the implementation of a behavioral algorithm based on user propensity. However, user preferences for automated driving patterns exhibit considerable variation, making it essential to conduct an in‐depth investigation into the preferred automated driving patterns corresponding to users’ propensity. In this study, we confirmed that the characteristics of preferred automated driving patterns can be deduced from comprehensive driving propensities, which were derived by combining inherent driving propensities with simulator‐based driving behavior characteristics using the fuzzy logic method. This study confirmed that in the era of automated driving, the preferred automated driving patterns may vary depending on the propensity from the user’s perspective. Considering these differences, it is meaningful in which it suggests the need for automated driving motions to be implemented based on individual preferences that appear according to human factors such as user propensity.

Black ice is one of the main causes of traffic accidents in winter, and warning signs for black ice are generally ineffective because of the lack of credible information. To overcome this limitation, new warning signs for black ice were developed using materials that change color in response to different temperatures. The performance and effects of the new signs were investigated by conducting driver behavior analysis. To this end, driving simulator experiments were conducted with 37 participants for two different rural highway sections, i.e., a curve and a tangent. The analysis results of the driving behavior and visual behavior experiments showed that the conventional signs had insufficient performance in terms of inducing changes in driving behavior for safety. Meanwhile, the new signs actuated by weather conditions offered a statistically significant performance improvement. Typically, driver showed two times higher speed deceleration when they fixed eyes on the new weather-actuated warning sign (12.80 km/h) compared to the conventional old warning sign (6.84 km/h) in the curve segment. Accordingly, this study concluded that the new weather-actuated warning signs for black ice are more effective than the conventional ones for accident reduction during winters.

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles.

Personal mobility vehicles (PMVs) are the potential future travel mode, typically for “first-mile and last-mile trips,” in urban areas. Accordingly, the popularity of PMVs is increasing, and the purpose of using PMVs is diversifying in South Korea. Before revision of the law concerning PMVs, the driving ways and roads for PMVs were not defined by regulations in South Korea. Therefore, most PMV users preferred to drive on sidewalks and bicycle lanes to avoid collisions with larger vehicles. Furthermore, PMVs have been involved in many accidents. To solve these problems, the government of South Korea revised the Road Traffic Act to permit PMVs to travel on bicycle lanes and considered building a new type of lane for PMVs based on the results of field experiments conducted in this study. In these experiments, the minimum turning radius with various speeds, driving performance on roads with vertical alignments, lateral placements in curved sections were examined. In addition, conflicts and inconvenience of PMVs driving on bicycle lanes were tested. In the experiments, three different types of PMVs—electric scooter (e-scooter), electric unicycle (e-unicycle), and segway—were used. Through the experiments, the feasibility of using PMVs on bicycle lanes was assessed, and the design criteria for PMV lanes were derived.

While automated-driving technology is advancing rapidly, human-centered research is still in its early stages. Research on negative user responses to automated driving is particularly limited in complex roadway environments such as roundabouts, where driving decisions typically depend on driver judgment and traffic conditions. In these environments, automated-driving vehicles may exhibit unstable behaviors, such as sudden stops or forced intersection entries. Since successful interaction between users and automated systems is critical for widespread adoption, understanding when and how automated driving negatively affects users is essential. This study investigated user psychological responses and corresponding physiological changes during unstable automated-driving situations. Using a virtual environment driving simulator, we compared two scenarios: sensor-only automated driving (A.D(S)), which exhibited unstable driving patterns; and cooperative automated driving (A.D(C)), which achieved more stable performance through infrastructure communication. We analyzed the responses of 30 participants using electromyography (EMG) measurements and pupil diameter tracking, supplemented by qualitative evaluations. Results showed that A.D(S) participants experienced higher levels of frustration during prolonged waiting times compared to A.D(C) participants. In addition, sudden braking events elicited startle responses characterized by pupil dilation and elevated arm-muscle EMG readings. This research advances our understanding of how automated-driving behaviors affect user experience and emphasizes the importance of human factors in the development of automated-driving technologies.

E-scooters have rapidly become a popular option for first- and last-mile mobility, yet their integration into urban transportation systems has raised significant safety concerns. This study investigates the feasibility of permitting E-scooter riding on sidewalks under controlled conditions to minimize pedestrian conflicts. Analysis of E-scooter crashes in Daejeon, South Korea, showed that 98.09% of crashes were caused by rider negligence, with “Failure to Fulfill Safe Driving Duty” as the leading factor. To investigate the applicability of safe sidewalk usage, a VR-based simulator experiment was conducted with 41 participants across four scenarios with varying sidewalk widths and pedestrian densities, under speed limits of 10, 15, and 20 km/h. Riding behaviors—including speed stability, braking, steering, and conflict frequency—and gaze behaviors were measured. Results showed that riding at 10 km/h improved riding stability and minimized conflicts. Regression analysis identified pedestrian density as the strongest predictor of conflicts, followed by sidewalk width and riding speed. These findings suggest specific policy needs: ensuring a minimum sidewalk width of 4 m for safe shared use, restricting operation to environments with low-to-moderate pedestrian density, and implementing a 10 km/h speed limit. This study provides evidence-based recommendations for safer integration of E-scooters into pedestrian environments.