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.

翻译：本文提出了一种专为自动驾驶设计的轨迹预测模型，专注于捕捉动态交通场景中的复杂交互，且无需依赖高精度地图。该模型名为MFTraj，利用历史轨迹数据，并结合一种基于新型动态几何图的行为感知模块。其核心是一个自适应结构感知交互图卷积网络，能够捕捉道路使用者的位置与行为特征，保留时空细节。通过线性注意力机制的增强，该模型实现了计算效率的提升和参数量的减少。在Argoverse、NGSIM、HighD和MoCAD数据集上的评估表明，MFTraj具有鲁棒性和适应性，即使在数据受限场景下，也无需高精度地图或矢量化地图等额外信息，即可超越众多基准模型。值得注意的是，即使在大量数据缺失的情况下，它仍能保持与大多数现有最先进模型相当的竞争力。实验结果与方法表明，该研究在自动驾驶轨迹预测领域取得了显著进展，为构建更安全、更高效的自动驾驶系统铺平了道路。