| Autor: |
Dengfeng Zhao, Junjian Hou, Yudong Zhong, Wenbin He, Zhijun Fu, Fang Zhou |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
World Electric Vehicle Journal, Vol 13, Iss 11, p 207 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2032-6653 |
| DOI: |
10.3390/wevj13110207 |
| Popis: |
Driver identification is very important to realizing customized service for drivers and road traffic safety for electric vehicles and has become a research hotspot in the field of modern automobile development and intelligent transportation. This paper presents a comprehensive review of driver identification methods. The basic process of driver identification task is proposed as four steps, the advantages and disadvantages of different data sources for driver identification are analyzed, driver identification models are divided into three categories, and the characteristics and research progress of driver identification models are summarized, which can provide a reference for further research on driver identification. It is concluded that on-board sensor data in the natural driving state is objective and accurate and could be the main data source for driver identification. Emerging technologies such as big data, artificial intelligence, and the internet of things have contributed to building a deep learning hybrid model with high accuracy and robustness and representing an important gradual development trend of driver identification methods. Developing a driver identification method with high accuracy, real-time performance, and robustness is an important development goal in the future. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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