Pedestrian motion prediction is of critical significance for intelligent and safe autonomous driving systems design. Human movement is by nature highly non-deterministic and multi-modal. Particularly, humans’ travel goals and their behavioral decisions interact with each other. In this work, we present Goal Interaction Conditioned Network GICNet for flexible and accurate human trajectory forecasting. Social influence, multi-modality, and goal constraints have been incorporated into GICNet to infer socially compliant human trajectories. The approach operates in three key stages: learning a probability distribution of motion intentions from historical data, grouping pedestrians and modeling goal-goal interactions using a masked Graph Attention Network (GAT), and integrating intention with motion history for prediction. Additionally, we present a novel iterative pooling method to adaptively fuse the impact of different pedestrian attributes during trajectory generation, enhancing robustness to neighbor misidentification. We demonstrate that GICNet generates realistic multi-modal trajectories, and improves the state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by \(\sim \) 6.7% and on ETH-UCY benchmark by \(\sim \) 23.0% under the Best-of-20 evaluation protocol, significantly outperforming existing probabilistic models in dense interaction scenarios.