Incorporating Roadside Traffic Data to Predict Lane Change Behaviors within Weaving Segment for Automated Vehicles

The weaving segment is the bottleneck of expressways, as onramp and offramp traffic intermingles. While numerous studies have focused on predicting lane change behaviors within these segments, many existing approaches fail to exploit the synergistic potential of multisource perception data. This study proposes a novel two-stage prediction model that integrates onboard and roadside perception for enhanced lane change prediction within weaving segments. The proposed methodology comprises sequential intention recognition and trajectory prediction stages. In the first stage, the model leverages roadside perception to assist in inferring early lane change intentions through latent traffic information. In the second stage, it predicts precise lane-changing trajectories with the predicted lane change intentions as prior knowledge. The model’s performance was validated using field trajectory data. Specifically, the F1-score is improved by 4.77% for the right lane change intention category. The average displacement error (ADE) and final displacement error (FDE) of predicted trajectories are reduced by 15.3% and 10.1%, respectively. This study provides a fresh perspective on lane change prediction within weaving segments and achieves inspiring results.