Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
This work addresses a domain-specific problem for intelligent transportation systems, but it appears incremental as it builds on existing methods like attention mechanisms.
The paper tackled driver behavior modeling at urban intersections by proposing a canonical correlation analysis-based framework, achieving better performance than previous methods in comparative studies.
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.