Probabilistic and Interaction-Aware Trajectory Prediction Using Score-Based Diffusion Models

Understanding human motion is fundamental to the development of intelligent systems capable of seamless interaction with people. Trajectory prediction is a critical component in domains, such as intelligent transportation, surveillance, and human–robot collaboration. However, accurately forecasting human movement remains a significant challenge due to its inherently uncertain and multimodal nature. In this work, we propose a deep neural network that models agent dynamics and predicts future trajectories by representing them as a probabilistic multimodal distribution. To effectively capture the stochasticity of human behavior, our method employs a score-based diffusion model that learns to generate realistic trajectory samples by denoising latent representations. In addition, we introduce a novel social attention mechanism designed to model complex interagent interactions, further improving predictive performance. We validate our approach in both pedestrian and marine vessel trajectory datasets, demonstrating its superior ability to capture social dynamics and forecast diverse plausible future outcomes. Extensive experiments and ablation studies confirm the robustness, generalizability, and accuracy of our framework in varied real-world environments.