  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.
