Trajectory prediction is crucial for autonomous driving as it aims to forecast the future movements of traffic participants. Traditional methods usually perform holistic inference on the trajectories of agents, neglecting the differences in prediction difficulty among agents. This paper proposes a novel Difficulty-Guided Feature Enhancement Network (DGFNet), which leverages the prediction difficulty differences among agents for multi-agent trajectory prediction. Firstly, we employ spatio-temporal feature encoding and interaction to capture rich spatio-temporal features. Secondly, a difficulty-guided decoder is used to control the flow of future trajectories into subsequent modules, obtaining reliable future trajectories. Then, feature interaction and fusion are performed through the future feature interaction module. Finally, the fused agent features are fed into the final predictor to generate the predicted trajectory distributions for multiple participants. Experimental results demonstrate that our DGFNet achieves state-of-the-art performance on the Argoverse 1\&2 motion forecasting benchmarks. Ablation studies further validate the effectiveness of each module. Moreover, compared with SOTA methods, our method balances trajectory prediction accuracy and real-time inference speed.

翻译：轨迹预测对于自动驾驶至关重要，其旨在预测交通参与者的未来运动。传统方法通常对智能体的轨迹进行整体推断，忽略了不同智能体间预测难度的差异。本文提出了一种新颖的难度引导特征增强网络，该网络利用智能体间的预测难度差异进行多智能体轨迹预测。首先，我们使用时空特征编码与交互来捕获丰富的时空特征。其次，采用一个难度引导解码器来控制未来轨迹信息流向后续模块，从而获得可靠的未来轨迹。接着，通过未来特征交互模块进行特征交互与融合。最后，将融合后的智能体特征输入最终预测器，以生成多个参与者的预测轨迹分布。实验结果表明，我们的DGFNet在Argoverse 1&2运动预测基准上取得了最先进的性能。消融研究进一步验证了各模块的有效性。此外，与现有最优方法相比，我们的方法在轨迹预测精度与实时推理速度之间取得了良好平衡。