التفاصيل البيبلوغرافية
| العنوان: |
Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System |
| المؤلفون: |
Jingliang Lv, Yu Wang, Haiyue Fu, Yulong Pei, Zhijie Xie |
| سنة النشر: |
2024 |
| مصطلحات موضوعية: |
Science Policy, Mental Health, Space Science, Environmental Sciences not elsewhere classified, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, shows broad potential, multiple perception capabilities, convolutional neural network, analyze temporal information, reduce driver fatigue, driver identification experiments, driver assistance technology, sensor triggering sequence, based triboelectric sensor, kresling origami structure, hod ), identification, different driving behaviors, driver ’, assisted sensor, complex structure, idams ), study develops, severely violate, safety warning, results show, results indicate, pressing time, pressing force |
| الوصف: |
Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver’s pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human–computer interaction. |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/media/Deep-Learning-Assisted_Sensor_with_Multiple_Perception_Capabilities_for_an_Intelligent_Driver_Assistance_Monitoring_System/25356142 |
| DOI: |
10.1021/acsami.3c15956.s002 |
| الاتاحة: |
https://doi.org/10.1021/acsami.3c15956.s002
https://figshare.com/articles/media/Deep-Learning-Assisted_Sensor_with_Multiple_Perception_Capabilities_for_an_Intelligent_Driver_Assistance_Monitoring_System/25356142 |
| Rights: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.3A42E25D |
| قاعدة البيانات: |
BASE |