A Vehicle Lane-Change Lateral Motion and Behavior Predictive Model

In mixed traffic scenarios where human-driven vehicles (HDVs) and autonomous/automated vehicles (AVs) of various levels coexist, the lateral behaviors of all vehicles—such as lane-change maneuvers—are influenced by surrounding vehicles. Predicting these behaviors before the lane-change maneuvers is essential for improving the decision-making and path-planning strategies of AVs, while offering valuable driving references for human drivers. This paper proposes a universal lateral motion prediction model applicable for both HDVs and AVs in mixed highway traffic. First, variables at the onset of the lane change are analyzed, and their respective relationships with lane-change time, peak lateral speed, and the time to reach peak lateral speed are established. Next, a mapping function is designed to preprocess raw variables (e.g., time to collision) and connect it with the proposed prediction model for enhanced interpretability. To exemplify the predictability of the model, two drivers were recruited for repeated lane change tests in diverse scenarios, generating raw data for their model parameter estimation. Experimental results demonstrate that the proposed model provides reliable lateral motion prediction for vehicles under various scenarios and drivers, establishing a foundation for enhancing decision-making and path-planning in AVs within mixed traffic environments.