Road user trajectory prediction in dynamic environments is a challenging but
crucial task for various applications, such as autonomous driving. One of the
main challenges in this domain is the multimodal nature of future trajectories
stemming from the unknown yet diverse intentions of the agents. Diffusion
models have shown to be very effective in capturing such stochasticity in
prediction tasks. However, these models involve many computationally expensive
denoising steps and sampling operations that make them a less desirable option
for real-time safety-critical applications. To this end, we present a novel
framework that leverages diffusion models for predicting future trajectories in
a computationally efficient manner. To minimize the computational bottlenecks
in iterative sampling, we employ an efficient sampling mechanism that allows us
to maximize the number of sampled trajectories for improved accuracy while
maintaining inference time in real time. Moreover, we propose a scoring
mechanism to select the most plausible trajectories by assigning relative
ranks. We show the effectiveness of our approach by conducting empirical
evaluations on common pedestrian (UCY/ETH) and autonomous driving (nuScenes)
benchmark datasets on which our model achieves state-of-the-art performance on
several subsets and metrics.

在动态环境中，道路用户轨迹预测是一个具有挑战性但十分关键的任务，特别适用于自动驾驶等各种应用。本研究提出了一种新的框架，以计算高效的方式利用扩散模型来预测未来的轨迹。我们展示了该方法在常见的行人和自动驾驶基准数据集上取得了最先进的性能。