Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis

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.

[1]  Vijay Gadepally,et al.  A Framework for Estimating Driver Decisions Near Intersections , 2014, IEEE Transactions on Intelligent Transportation Systems.

[2]  Yu Yao,et al.  Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections , 2019, IEEE Transactions on Intelligent Transportation Systems.

[3]  Qiuping Xu.  Canonical correlation Analysis , 2014 .

[4]  Junmin Wang,et al.  An Autonomous T-Intersection Driving Strategy Considering Oncoming Vehicles Based on Connected Vehicle Technology , 2019, IEEE/ASME Transactions on Mechatronics.

[5]  Jianwei Gong,et al.  A Comparative Study on Transferable Driver Behavior Learning Methods in the Lane-Changing Scenario , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[6]  H. Sung.  Gaussian Mixture Regression and Classification , 2004 .

[7]  Masayoshi Tomizuka,et al.  INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps , 2019, ArXiv.

[8]  Krzysztof Czarnecki,et al.  Trajectory prediction of traffic agents at urban intersections through learned interactions , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[9]  Petros A. Ioannou,et al.  Personalized Driver Assistance for Signalized Intersections Using V2I Communication , 2016, IEEE Transactions on Intelligent Transportation Systems.

[10]  Martin Guha,et al.  Encyclopedia of Statistics in Behavioral Science , 2006 .

[11]  Gang Wang,et al.  Development and Evaluation of Two Learning-Based Personalized Driver Models for Pure Pursuit Path-Tracking Behaviors , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[12]  Zhiheng Li,et al.  Modeling Vehicle Interactions via Modified LSTM Models for Trajectory Prediction , 2019, IEEE Access.

[13]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[14]  Yue Meng,et al.  Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Junqiang Xi,et al.  Learning Driver-Specific Behavior for Overtaking: A Combined Learning Framework , 2018, IEEE Transactions on Vehicular Technology.

[16]  Dongpu Cao,et al.  Virtual-to-Real Knowledge Transfer for Driving Behavior Recognition: Framework and a Case Study , 2019, IEEE Transactions on Vehicular Technology.

[17]  Masayoshi Tomizuka,et al.  Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving , 2019, IEEE Transactions on Intelligent Transportation Systems.

[18]  Bin Shuai,et al.  Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles , 2019, Applied Energy.

[19]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[20]  Jianwei Gong,et al.  Transferable Driver Behavior Learning via Distribution Adaption in the Lane Change Scenario* , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[21]  Dongpu Cao,et al.  Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment , 2020, IEEE Transactions on Intelligent Transportation Systems.