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.

翻译：动态环境中的道路使用者轨迹预测是自动驾驶等应用面临的挑战性关键任务。该领域主要难点在于未来轨迹的多模态特性——源于智能体未知且多样的行为意图。扩散模型在捕捉此类预测任务的随机性方面展现出卓越性能，但其涉及大量高计算成本的去噪步骤与采样操作，使其在实时安全关键型应用中缺乏竞争力。为此，我们提出一种新型框架，以计算高效的方式利用扩散模型预测未来轨迹。为最小化迭代采样的计算瓶颈，我们采用高效采样机制，在保持实时推理速度的同时最大化采样轨迹数量以提升预测精度。此外，我们提出评分机制，通过分配相对等级筛选最可能的轨迹。在主流行人轨迹（UCY/ETH）与自动驾驶（nuScenes）基准数据集上的实证评估表明，该模型在多个子集与评估指标上均达到当前最优性能。