A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives
Driving style recognition provides an effective way to understand human driving behaviors and thereby plays an important role in the automotive sector. However, most works fail to consider the influence of deploying the recognition results on the vehicle side, which requires real-time recognition performance. To facilitate the application of driving styles in automotive, the authors survey related advances in driving style recognition along short- and long-term pipelines. The authors first defined short- and long-term driving styles and then described the input data used by the recognition models and related data-processing techniques. Furthermore, the authors also revisited existing evaluation metrics for different recognition algorithms. Finally, the authors discussed the potential applications of driving style recognition in intelligent vehicles.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
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Supplemental Notes:
- Copyright © 2024, IEEE.
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Authors:
- Chu, Hongqing
- Zhuang, Hejian
- Wang, Wenshuo
- Na, Xiaoxiang
- Guo, Lulu
- Zhang, Jia
- Gao, Bingzhao
- Chen, Hong
- Publication Date: 2023-11
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 4599-4612
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Serial:
- IEEE Transactions on Intelligent Vehicles
- Volume: 8
- Issue Number: 11
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2379-8858
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857
Subject/Index Terms
- TRT Terms: Automatic data collection systems; Autonomous vehicles; Driving behavior; Physiology; Task analysis
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01905983
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 26 2024 10:02AM