A Data-Driven and Kinematics-Aware Approach to Reconstruct Full-Sample Vehicle Trajectories From Low-Quality Roadside Perception Data

Full-sample vehicle trajectories collected by roadside sensors are pivotal for advancements in active traffic management and vehicle-highway automation applications. However, sensor inefficiencies and environmental factors often lead to incomplete and noisy data, impeding practical applications. To overcome this hurdle, we propose a data-driven and kinematics-aware method for reconstructing full-sample vehicle trajectories. It employs an encoder-decoder architecture, where the data-driven encoder utilizes graph-based map modeling to capture road network characteristics and models multi-vehicle interactions based on real-world driving attention patterns. The model-driven decoder integrates conditional variational autoencoder with a vehicle kinematic model, to ensure that the generated trajectories adhere closely to vehicle kinematic principles. Experiments on large-scale trajectory datasets demonstrate that our approach exhibits good performance in trajectory reconstruction. Furthermore, case studies on the DAIR-V2X dataset indicate that our approach could improve raw trajectory data quality and downstream prediction accuracy.