  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.
