CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
This addresses trajectory prediction for autonomous vehicles in V2X scenarios, representing an incremental improvement with specific gains.
The paper tackles trajectory prediction instability in autonomous driving by proposing CoPAD, a lightweight framework that fuses multi-source data from vehicles and infrastructure, achieving state-of-the-art performance on the DAIR-V2X-Seq dataset.
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (PTA) module, mode attention module and anchor-oriented decoder (AoD). It effectively performs early fusion on multi-source trajectory data from vehicles and road infrastructure, enabling the trajectories with high completeness and accuracy. The PTA module can efficiently capture potential interaction information among historical trajectories, and the mode attention module is proposed to enrich the diversity of predictions. Additionally, the decoder based on sparse anchors is designed to generate the final complete trajectories. Extensive experiments show that CoPAD achieves the state-of-the-art performance on the DAIR-V2X-Seq dataset, validating the effectiveness of the model in cooperative trajectory prediction in V2X scenarios.