Dynamic Sensing and Recognition of Lateral Driving Behavior for Connected Vehicles Using Machine Learning and Integrated Learning Methods

Lateral driving behavior represents the lateral movement of a vehicle while traveling on the road, such as changing lanes and turning. Lateral driving behavior monitoring provides significant support for automatic driving, vehicle-circuit coordination, and intelligent traffic management. Identifying driving behavior through mobile sensing techniques, such as smartphone and vehicle-mounted terminals, has gained attention in recent years. In this paper, a dynamic lateral driving behavior sensing and recognition system is proposed utilizing the On-Board Unit (OBU) of current Vehicle-Infrastructure Cooperation Systems (VICS). After a comprehensive analysis of the vehicle’s lateral driving behavior, machine learning and integrated learning methods are applied for lateral driving behavior recognition. The results show that the proposed method in this paper can effectively distinguish different lateral driving behaviors and achieve a high accuracy rate. The outcomes of this work could provide a viable solution for effective fine-grained traffic information sensing and monitoring in vehicle-connected scenarios.