A Proactive Risk Prediction Framework for Cut-In Maneuvers Incorporating Inherent Driving Styles

The cut-in maneuver is a common high-interaction behavior between vehicles, where improper execution may lead to driving risks and is closely associated with the vehicle’s inherent driving style. Existing driving risk prediction studies lack targeted modeling for this typical maneuver, while current driving style modeling approaches often fail to capture stable and inherent behavioral traits. This study proposes a classification method for inherent driving styles and develops a proactive prediction framework for cut-in risk, which is validated and analyzed using wide-area trajectory data. The results indicate that: (a) the indicator system constructed based on car-following, lane-changing, and interaction characteristics effectively captures inherent driving traits, and the recognition model performs well when the number of style clusters is set to three; (b) the cut-in risk prediction model developed using LightGBM achieves optimal predictive performance, with a fixed observation window and a 2-second lead time offering the most practical feasibility for proactive warning applications; (c) incorporating inherent driving style into the model reduces the prediction error by 3.9% and supports more targeted decision-making. The proposed framework enables proactive recognition of cut-in risks from surrounding vehicles by identifying and sharing inherent driving style information in a connected environment, thereby supporting ego vehicle intervention and decision-making to actively adjust its behavior and reduce driving risk.