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1,697 results on '"Yasuda, Hiroshi"'

1. Portobello: Extending Driving Simulation from the Lab to the Road

Author

Bu, Fanjun, Li, Stacey, Goedicke, David, Colley, Mark, Sharma, Gyanendra, Yasuda, Hiroshi, and Ju, Wendy

Subjects
Computer Science - Human-Computer Interaction
Abstract

In automotive user interface design, testing often starts with lab-based driving simulators and migrates toward on-road studies to mitigate risks. Mixed reality (XR) helps translate virtual study designs to the real road to increase ecological validity. However, researchers rarely run the same study in both in-lab and on-road simulators due to the challenges of replicating studies in both physical and virtual worlds. To provide a common infrastructure to port in-lab study designs on-road, we built a platform-portable infrastructure, Portobello, to enable us to run twinned physical-virtual studies. As a proof-of-concept, we extended the on-road simulator XR-OOM with Portobello. We ran a within-subjects, autonomous-vehicle crosswalk cooperation study (N=32) both in-lab and on-road to investigate study design portability and platform-driven influences on study outcomes. To our knowledge, this is the first system that enables the twinning of studies originally designed for in-lab simulators to be carried out in an on-road platform., Comment: CHI 2024

Published
2024

2. Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference

Author

Sumner, Emily S, DeCastro, Jonathan, Costa, Jean, Gopinath, Deepak E, Kimani, Everlyne, Hakimi, Shabnam, Morgan, Allison, Best, Andrew, Nguyen, Hieu, Brooks, Daniel J, Haq, Bassam ul, Patrikalakis, Andrew, Yasuda, Hiroshi, Sieck, Kate, Balachandran, Avinash, Chen, Tiffany, and Rosman, Guy

Subjects
Computer Science - Human-Computer Interaction
Abstract

Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Although it is widely recognized that certain cognitive factors, such as impulsivity and inhibitory control, are related to risky driving behavior, play a significant role in on-road risk-taking, existing systems fail to leverage such factors. Varying levels of these cognitive factors could influence the effectiveness and acceptance of driver safety interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are triggered based on a learned recurrent neural network. The network is trained from a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using a high-fidelity vehicle motion simulator, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to make instantaneous determinations on whether or not to engage a driver safety interface. This interface aims to decrease a driver's speed during yellow lights and reduce their inclination to run through them., Comment: 12 pages, 7 figures

Published
2024

3. Clinical practice guidelines for molecular tumor marker, 2nd edition review part 2

Author

Kikuchi, Yoshinori, Shimada, Hideaki, Yamasaki, Fumiyuki, Yamashita, Taku, Araki, Koji, Horimoto, Kohei, Yajima, Satoshi, Yashiro, Masakazu, Yokoi, Keigo, Cho, Haruhiko, Ehira, Takuya, Nakahara, Kazunari, Yasuda, Hiroshi, Isobe, Kazutoshi, Hayashida, Tetsu, Hatakeyama, Shingo, Akakura, Koichiro, Aoki, Daisuke, Nomura, Hiroyuki, Tada, Yuji, Yoshimatsu, Yuki, Miyachi, Hayato, Takebayashi, Chiaki, Hanamura, Ichiro, and Takahashi, Hiroyuki

Published
2024
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5. Detection Threshold of Audio Haptic Asynchrony in a Driving Context

Author

Sharma, Gyanendra, Yasuda, Hiroshi, and Kuehner, Manuel

Subjects
Computer Science - Human-Computer Interaction, 91E30, H.1.2
Abstract

In order to provide perceptually accurate multimodal feedback during driving situations, it is vital to understand the threshold at which drivers are able to recognize asyncrony between multiple incoming Stimuli. In this work, we investigated and report the \textit{detection threshold} (DT) of asynchrony between audio and haptic feedback, in the context of a force feedback steering wheel. We designed the experiment to loosely resemble a driving situation where the haptic feedback was provided through a steering wheel (\textit{Sensodrive}), while the accompanying audio was played through noise cancelling headphones. Both feedbacks were designed to resemble rumble strips, that are generally installed on the side of major roadways as a safety tool. The results indicate that, for $50\%$ of the participants, asynchrony was detectable outside the range of -75 ms and 110 ms, where the former is related to perceiving audio before haptic and vice versa for the latter. We were also able to concur with previous studies, which state that latency is perceivable at a lower threshold when audio precedes haptic stimuli., Comment: 8 pages, 9 figures

Published
2023

6. Continuous Visual Feedback of Risk for Haptic Lateral Assistance

Author

Sharma, Gyanendra, Yasuda, Hiroshi, and Kuehner, Manuel

Subjects
Computer Science - Human-Computer Interaction
Abstract

Lateral assistance systems are a significant part of the currently existing Advanced Driving Assistance System (ADAS). In most cases, such systems provide intermittent audio and haptic feedback rather than continuous feedback, making it difficult for drivers to form a clear mental model. In this paper, we propose continuous visual feedback for the lateral assistance system by leveraging the Head-Up Display (HUD) alongside haptic feedback through the steering wheel. The HUD provides visualization of the risk profile underlying the haptic feedback. We hypothesize that our proposed visualization helps form a clear mental model and improves the system's acceptance. We conduct a user study on a simulated version of a car driving on a two-lane freeway and compare the haptic lateral assistance system with and without the visualization on the HUD. While both conditions received high acceptance scores, there was no significant gain or deterioration in acceptance between them. We discuss potential improvements in the visualization based on anticipation performance and qualitative feedback from users.

Published
2023

25. CySec: Cybersecurity Review Program for Professionals in Japan

Author

Kakizaki, Yoshio, Sasaki, Ryoichi, Okochi, Tomohide, Yasuda, Hiroshi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Li, Kin Fun, editor, Enokido, Tomoya, editor, and Takizawa, Makoto, editor

Published
2021
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40. Impact of the COVID‐19 pandemic on the time to emergency endoscopy and clinical outcomes in patients with upper gastrointestinal bleeding

Author

Komatsu, Takumi, primary, Sato, Yoshinori, additional, Kuroki, Yuichiro, additional, Yoshida, Yoshihito, additional, Aoyama, Natsumi, additional, Iijima, Yoshihiko, additional, Nakamoto, Yusuke, additional, Kato, Masaki, additional, Kiyokawa, Hirofumi, additional, Tanabe, Kenichiro, additional, Matsunaga, Koutaro, additional, Maehata, Tadateru, additional, Yasuda, Hiroshi, additional, Matsumoto, Nobuyuki, additional, and Tateishi, Keisuke, additional

Published
2023
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43. Interlaboratory comparison of electron paramagnetic resonance tooth enamel dosimetry with investigations of the dose responses of the standard samples

Author

Toyoda, Shin, primary, Inoue, Kazuhiko, additional, Yamaguchi, Ichiro, additional, Hoshi, Masaharu, additional, Hirota, Seiko, additional, Oka, Toshitaka, additional, Shimazaki, Tatsuya, additional, Mizuno, Hideyuki, additional, Tani, Atsushi, additional, Yasuda, Hiroshi, additional, Gonzales, Chryzel A B, additional, Okutsu, Kenichi, additional, Takahashi, Atsushi, additional, Tanaka, Nao, additional, and Todaka, Azumi, additional

Published
2023
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