@inproceedings{41ae1267a8174f8083a97b4cebd04e92,
title = "Driver behavior modelling at the urban intersection via canonical correlation analysis",
abstract = "The urban intersection is a typically dynamic and complex scenario for intelligent vehicles, which exists a variety of driving behaviors and traffic participants. Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS). Previous researches mainly focus on using attention mechanism to model the degree of correlation. In this research, a canonical correlation analysis (CCA)-based framework is proposed. The value of canonical correlation is used for feature selection. Gaussian mixture model and Gaussian process regression are applied for driver behavior modelling. Two experiments using simulated and naturalistic driving data are designed for verification. Experimental results are consistent with the driver' s judgment. Comparative studies show that the proposed framework can obtain a better performance.",
keywords = "Canonical Correlation Analysis, Driver behaviors, Urban intersections",
author = "Zirui Li and Chao Lu and Cheng Gong and Jianwei Gong and Jinghang Li and Lianzhen Wei",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Conference on Unmanned Systems, ICUS 2020 ; Conference date: 27-11-2020 Through 28-11-2020",
year = "2020",
month = nov,
day = "27",
doi = "10.1109/ICUS50048.2020.9274914",
language = "English",
series = "Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "564--569",
booktitle = "Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020",
address = "United States",
}
