Analysis of Driver's Cognitive Characteristics for Speed Control Using Deep Learning
It is important to elucidate what the drivers perceive to operate the vehicle in order to apply the driver characteristics to self-driving cars. Therefore, the authors proposed the driver's cognitive-operation model using deep learning. Then, they used the proposed model to analyze the cognitive areas of drivers that influence the speed control of the vehicle. The authors verified whether the results obtained in this study are consistent with the driver behavior that is proven by the prior studies. As a result, they showed the reliability of the proposed driver's cognitive-operation model that can extract the driver behavior.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
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Authors:
- Osugi, Ryusei
- Okafuji, Yuki
- Wada, Takahiro
- Publication Date: 2021-3
Language
- English
- Japanese
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: pp 355-362
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Serial:
- Transactions of Society of Automotive Engineers of Japan
- Volume: 52
- Issue Number: 1
- Publisher: Society of Automotive Engineers of Japan
- ISSN: 0287-8321
- EISSN: 1883-0811
- Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Advanced vehicle control systems; Automatic speed control; Automobile drivers; Automobile driving; Autonomous vehicles; Behavior; Cognition; Computer models; Machine learning
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01767962
- Record Type: Publication
- Source Agency: Japan Science and Technology Agency (JST)
- Files: TRIS, JSTAGE
- Created Date: Mar 23 2021 11:16AM