Development and validation of e-scooter riding behavior questionnaire (ERBQ) among Korean riders.

The main objective of this research was to develop a questionnaire that demonstrates elevated levels of reliability to assess the behaviour of e-scooter users. The researchers designed an E-scooter Riding Behaviour Questionnaire (ERBQ) with 27 items. This questionnaire aimed to assess the self-reported frequency of various e-scooter riding behaviours, including errors, violations and behaviours.

Exploring e-scooter riders' risky behaviour: survey, observation, and interview study.

This study explores e-scooter riders' risky behaviour and motivation based on survey, observation, and interview studies. 192 responses from an online survey showed that e-scooter riders' behaviours are categorised into errors, violations, defensive behaviour, and proactive safety behaviour. Statistical analysis revealed significant differences in riding behaviour depending on riders' characteristics and usage patterns.

Development and classification of autonomous vehicle's ambiguous driving scenario.

Human drivers are gradually being replaced by highly automated driving systems, and this trend is expected to persist. The response of autonomous vehicles to Ambiguous Driving Scenarios (ADS) is crucial for legal and safety reasons. Our research focuses on establishing a robust framework for developing ADS in autonomous vehicles and classifying them based on AV user perceptions.

Emerging Roles of Using Small Extracellular Vesicles as an Anti-Cancer Drug.

Small extracellular vesicles (sEVs) are emerging as a novel therapeutic strategy for cancer therapy. Tumor-cell-derived sEVs contain biomolecules that can be utilized for cancer diagnosis. sEVs can directly exert tumor-killing effects or modulate the tumor microenvironment, leading to anti-cancer effects.

Modeling takeover time based on non-driving-related task attributes in highly automated driving.

This study aims to investigate the effects of non-driving-related tasks (NDRTs) on the transition of control in highly automated driving (HAD) by investigating the effects of NDRT physical, visual, and cognitive attributes during transition of control. A conceptual model of the takeover process is proposed by dividing this process into motor and mental reactions. A laboratory experiment was conducted to evaluate the effects of each NDRT attribute on the corresponding stage of the process of taking over control.

The effects of takeover request modalities on highly automated car control transitions.

This study investigated the influences of takeover request (TOR) modalities on a drivers' takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers' take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive.

Assessment model for perceived visual complexity of automotive instrument cluster.

This research proposes an assessment model for quantifying the perceived visual complexity (PVC) of an in-vehicle instrument cluster. An initial study was conducted to investigate the possibility of evaluating the PVC of an in-vehicle instrument cluster by estimating and analyzing the complexity of its individual components. However, this approach was only partially successful, because it did not take into account the combination of the different components with random levels of complexity to form one visual display.