This paper introduces a trajectory prediction model tailored for autonomous
driving, focusing on capturing complex interactions in dynamic traffic
scenarios without reliance on high-definition maps. The model, termed MFTraj,
harnesses historical trajectory data combined with a novel dynamic geometric
graph-based behavior-aware module. At its core, an adaptive structure-aware
interactive graph convolutional network captures both positional and behavioral
features of road users, preserving spatial-temporal intricacies. Enhanced by a
linear attention mechanism, the model achieves computational efficiency and
reduced parameter overhead. Evaluations on the Argoverse, NGSIM, HighD, and
MoCAD datasets underscore MFTraj's robustness and adaptability, outperforming
numerous benchmarks even in data-challenged scenarios without the need for
additional information such as HD maps or vectorized maps. Importantly, it
maintains competitive performance even in scenarios with substantial missing
data, on par with most existing state-of-the-art models. The results and
methodology suggest a significant advancement in autonomous driving trajectory
prediction, paving the way for safer and more efficient autonomous systems.

这篇论文介绍了一种适用于自主驾驶的轨迹预测模型，重点是在动态交通场景中捕捉复杂的交互作用，而无需依赖高清地图。该模型被称为 MFTraj，利用历史轨迹数据结合新颖的基于动态几何图的行为感知模块。在其核心部分，一种自适应的结构感知交互图卷积网络捕捉道路用户的位置和行为特征，保留了空间 - 时间的复杂性。通过线性注意机制的增强，该模型实现了计算效率和降低参数开销。在 Argoverse、NGSIM、HighD 和 MoCAD 数据集上的评估证明了 MFTraj 的鲁棒性和适应性，在数据有限的场景下，甚至无需额外信息（如高清地图或矢量化地图），也能胜过许多基准模型。重要的是，即使在存在大量缺失数据的情况下，其性能与大多数现有的最先进模型相当。结果和方法表明，在自主驾驶轨迹预测方面取得了显著进步，为更安全、更高效的自主系统铺平了道路。