This paper introduces MFTraj, a trajectory prediction model designed for autonomous driving that does not rely on high-definition (HD) maps. MFTraj combines historical trajectory data with a novel dynamic geometric graph-based behavior-aware module to capture complex interactions in dynamic traffic scenarios. The core of the model is an adaptive structure-aware interactive graph convolutional network (GCN) that 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 demonstrate MFTraj's robustness and adaptability, outperforming numerous benchmarks even in data-challenged scenarios without 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 significant advancements in autonomous driving trajectory prediction, paving the way for safer and more efficient autonomous systems.This paper introduces MFTraj, a trajectory prediction model designed for autonomous driving that does not rely on high-definition (HD) maps. MFTraj combines historical trajectory data with a novel dynamic geometric graph-based behavior-aware module to capture complex interactions in dynamic traffic scenarios. The core of the model is an adaptive structure-aware interactive graph convolutional network (GCN) that 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 demonstrate MFTraj's robustness and adaptability, outperforming numerous benchmarks even in data-challenged scenarios without 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 significant advancements in autonomous driving trajectory prediction, paving the way for safer and more efficient autonomous systems.