  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.
