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. To achieve this, we conducted extensive literature reviews, in-depth interviews with industry experts, a comprehensive questionnaire survey, and factor analysis. We created 28 diverse ambiguous driving scenarios and examined 548 AV users' perspectives on moral, ethical, legal, utility, and safety aspects. Based on the results, we grouped ADS, with all of them having the highest user perception of safety. We classified these scenarios where autonomous vehicles yield to others as moral, bottleneck scenarios as ethical, cross-over scenarios as legal, and scenarios where vehicles come to a halt as utility-related. Additionally, this study is expected to make a valuable contribution to the field of self-driving cars by presenting new perspectives on policy and algorithm development, aiming to improve the safety and convenience of autonomous driving.

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. In this review, the application of sEVs as a diagnostic tool, drug delivery system, and active pharmaceutical ingredient for cancer therapy will be highlighted. The therapeutic efficacies of sEVs will be compared to conventional immune checkpoint inhibitors. Additionally, this review will provide strategies for sEV engineering to enhance the therapeutic efficacies of sEVs. As a bench-to-bedside application, we will discuss approaches to encourage good-manufacturing-practice-compliant industrial-scale manufacturing and purification of sEVs.

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. A prediction model was developed using the results of multiple linear regression analysis. Additionally, a validation experiment with nine NDRTs and a baseline condition was conducted to determine the extent to which the developed model explains the takeover time for each NDRT condition. The results showed that the timing aspects of the transition of control in HAD largely consist of participant motor reactions that are affected by the physical attributes of NDRTs.

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. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.

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. Therefore, a second study was conducted focusing on the effect of combining the different components. The results from the overall research enabled us to suggest a basis for quantifying the PVC of an in-vehicle instrument cluster based both on the PVCs of its components and on the integration effect.