论文标题
通过规范相关分析在城市交叉口进行驾驶员行为建模
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
论文作者
论文摘要
城市十字路口是智能车辆的典型动态且复杂的场景，它存在各种驾驶行为和交通参与者。准确地对交叉路口进行驾驶员行为进行建模对于智能运输系统（ITS）至关重要。先前的研究主要集中于使用注意机制来建模相关程度。在这项研究中，提出了基于规范的相关分析（CCA）的框架。规范相关的值用于特征选择。高斯混合模型和高斯过程回归用于驾驶员行为建模。使用模拟和自然主义驾驶数据的两个实验设计用于验证。实验结果与驾驶员的判断一致。比较研究表明，所提出的框架可以获得更好的性能。
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.
