用扩散模型生成更真实的人车轨迹，兼顾准确与环境避障。
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
- 将轨迹预测建模为去噪图像修复任务，引入地图引导项。
- 在nuScenes和PFSD数据集上达到或超越顶尖方法的精度与多样性。
- 适合自动驾驶、机器人路径规划等需避障的应用场景。
准确且多样地预测人或车辆轨迹是众多应用的关键任务。然而，现有模型常为提升多样性或精度而忽略重要约束，如与周围环境的碰撞规避。本文提出TrajDiffuse，一种基于规划的条件扩散轨迹预测方法。将轨迹预测视为去噪修复问题，并设计基于地图的引导项以指导扩散过程。该方法在nuScenes和PFSD数据集上实现了与当前最优（SOTA）相当或更优的精度与多样性，同时几乎完全遵守环境约束。通过广泛基准测试验证了其有效性。
原文摘要 · Abstract (English)
Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. However, many trajectory prediction models produce unreasonable trajectory samples that focus on improving diversity or accuracy while neglecting other key requirements, such as collision avoidance with the surrounding environment. In this work, we propose TrajDiffuse, a planning-based trajectory prediction method using a novel guided conditional diffusion model. We form the trajectory prediction problem as a denoising impaint task and design a map-based guidance term for the diffusion process. TrajDiffuse is able to generate trajectory predictions that match or exceed the accuracy and diversity of the SOTA, while adhering almost perfectly to environmental constraints. We demonstrate the utility of our model through experiments on the nuScenes and PFSD datasets and provide an extensive benchmark analysis against the SOTA methods.
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