Author(s)

Yu Quan ¹, Yangchao Jie ¹, Wangchuan Yang ¹

Affiliation(s)

¹ School of Electrical and Control Engineering, North China University of Technology, Beijing, China

Corresponding Author

Yangchao Jie

ABSTRACT

Signalized intersections serve as critical hubs in urban road networks. At intersections without dedicated right-turn phases, frequent interactions occur between right-turning vehicles and pedestrians/non-motorized vehicles. Suboptimal interactions may lead to traffic conflicts, significantly compromising travel safety and operational efficiency. This study categorizes right-turning vehicle intentions into three types: full-stop yielding, deceleration yielding, and non-yielding behaviors. Influencing factors are classified into agent-related factors and environmental factors, with input features for intention recognition models being selected through filter methods. Three intention recognition models-Support Vector Machine, Random Forest, and Logistic Regression-are developed to identify right-turning vehicle intentions. Through comprehensive evaluation metrics including accuracy and precision, comparative analysis reveals that the Logistic Regression model demonstrates optimal overall performance in precisely capturing right-turning vehicle intentions.

KEYWORDS

Right-Turning Vehicles; Machine Learning; Intention Recognition

CITE THIS PAPER

Yu Quan, Yangchao Jie, Wangchuan Yang, Machine Learning-Based Intention Recognition for Right-Turning Vehicles at Signalized Intersections. Advances in Computer, Signals and Systems (2025) Vol. 9: 144-156. DOI: http://dx.doi.org/10.23977/acss.2025.090218.

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