Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been made recently, the inherent trajectory uncertainty caused by various factors including the dynamic driving intends and the diverse driving scenarios still poses significant challenges to accurate trajectory prediction. To address this issue, we propose C2F-TP, a coarse-to-fine denoising framework for uncertainty-aware vehicle trajectory prediction. C2F-TP features an innovative two-stage coarse-to-fine prediction process. Specifically, in the spatial-temporal interaction stage, we propose a spatial-temporal interaction module to capture the inter-vehicle interactions and learn a multimodal trajectory distribution, from which a certain number of noisy trajectories are sampled. Next, in the trajectory refinement stage, we design a conditional denoising model to reduce the uncertainty of the sampled trajectories through a step-wise denoising operation. Extensive experiments are conducted on two real datasets NGSIM and highD that are widely adopted in trajectory prediction. The result demonstrates the effectiveness of our proposal.

翻译：准确预测车辆轨迹对于确保自动驾驶的安全性和可靠性至关重要。尽管近期已有大量研究工作，但由动态驾驶意图和多样化驾驶场景等多种因素引起的固有轨迹不确定性，仍然对精确的轨迹预测构成重大挑战。为解决这一问题，我们提出了C2F-TP，一种用于不确定性感知车辆轨迹预测的从粗到精去噪框架。C2F-TP采用了一种创新的两阶段从粗到精预测流程。具体而言，在时空交互阶段，我们提出了一个时空交互模块，用于捕捉车辆间的交互并学习一个多模态轨迹分布，从中采样出一定数量的含噪声轨迹。接着，在轨迹细化阶段，我们设计了一个条件去噪模型，通过逐步去噪操作来降低采样轨迹的不确定性。我们在轨迹预测领域广泛采用的两个真实数据集NGSIM和highD上进行了大量实验。结果证明了我们方案的有效性。