Abstract
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.
Funder
Key Research and Development Projects in Henan Province in 2022
Key Scientific and Technological project of Henan Province
Key Scientific Research Projects of Doctoral Fund of Zhengzhou University of light industry
Reference105 articles.
1. A technological overview & design considerations for developing electric vehicle charging stations
2. E-mobility: impacts and analysis of future transportation electrification market in economic, renewable energy and infrastructure perspective
3. Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification
4. Driver profiling by using LSTM networks with Kalman filtering;Klusek;Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV),2018
5. Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study;Azadani;Proceedings of the NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium. IEEE,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献