The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data. The effectiveness of the approach is assessed on large-scale datasets of real-world traffic scenarios, showing that our model outperforms several well-established methods in terms of prediction accuracy. By the incorporation of differential motion constraints on the model output, we illustrate that our model is capable of generating a diverse set of realistic future trajectories. Through the use of an interaction-aware guidance signal, we further demonstrate that the model can be adapted to predict the behavior of less cooperative agents, emphasizing its practical applicability under uncertain traffic conditions.

翻译：本文提出了一种基于扩散的多智能体轨迹预测生成模型。该模型能够捕捉交通参与者与环境之间的复杂交互，准确学习数据的多模态特性。我们在真实交通场景的大规模数据集上评估了该方法的有效性，结果表明，我们的模型在预测准确性上优于多种成熟方法。通过在模型输出中融入微分运动约束，我们展示了模型能够生成多样化且真实可行的未来轨迹。进一步地，通过利用交互感知引导信号，我们证明了该模型能够适应性地预测不合作代理的行为，凸显了其在不确定交通条件下的实际应用价值。