CoPAD通过多源轨迹融合与锚点解码，提升车联网环境下的协同预测精度。
CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
- 基于匈牙利算法和卡尔曼滤波实现多源轨迹早期融合
- 在DAIR-V2X-Seq数据集上达到当前最优性能
- 适合自动驾驶中复杂交通场景的协同轨迹预测
近年来，数据驱动的轨迹预测方法取得了显著进展，推动了自动驾驶的发展。然而，单车辆感知的不稳定性限制了轨迹预测效果。本文提出一种轻量级协同轨迹预测框架CoPAD，结合基于匈牙利算法和卡尔曼滤波的融合模块、历史时间注意力（PTA）模块、模式注意力模块以及锚点导向解码器（AoD），有效融合车辆与道路基础设施的多源轨迹数据，生成高完整性和准确性的轨迹。PTA模块可高效捕捉历史轨迹间的潜在交互信息，模式注意力模块增强预测多样性。基于稀疏锚点的解码器生成最终完整轨迹。大量实验表明，CoPAD在DAIR-V2X-Seq数据集上达到当前最优性能，验证了其在车联网场景下协同轨迹预测的有效性。
原文摘要 · Abstract (English)
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.
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