Predicting future trajectories of traffic agents accurately holds substantial importance in various applications such as autonomous driving. Previous methods commonly infer all future steps of an agent either recursively or simultaneously. However, the recursive strategy suffers from the accumulated error, while the simultaneous strategy overlooks the constraints among future steps, resulting in kinematically infeasible predictions. To address these issues, in this paper, we propose G2LTraj, a plug-and-play global-to-local generation approach for trajectory prediction. Specifically, we generate a series of global key steps that uniformly cover the entire future time range. Subsequently, the local intermediate steps between the adjacent key steps are recursively filled in. In this way, we prevent the accumulated error from propagating beyond the adjacent key steps. Moreover, to boost the kinematical feasibility, we not only introduce the spatial constraints among key steps but also strengthen the temporal constraints among the intermediate steps. Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory. Our G2LTraj significantly improves the performance of seven existing trajectory predictors across the ETH, UCY and nuScenes datasets. Experimental results demonstrate its effectiveness. Code will be available at https://github.com/Zhanwei-Z/G2LTraj.

翻译：准确预测交通参与者未来轨迹在自动驾驶等应用中具有重要价值。现有方法通常通过递归或同步方式推断代理的所有未来步骤。然而，递归策略存在误差累积问题，而同步策略忽视未来步骤之间的约束，导致生成运动学上不可行的预测。为解决这些问题，本文提出G2LTraj——一种即插即用的全局到局部轨迹预测生成方法。具体而言，我们首先生成一组均匀覆盖整个未来时间范围的全局关键步骤，然后递归地填充相邻关键步骤之间的局部中间步骤。通过这种方式，误差累积被限制在相邻关键步骤之间，避免进一步传播。此外，为提升运动学可行性，我们不仅引入关键步骤间的空间约束，还强化中间步骤间的时间约束。最终，为确保关键步骤粒度的最优性，我们设计了一种可选择性粒度策略，使其适配每条预测轨迹。我们的G2LTraj在ETH、UCY和nuScenes数据集上显著提升了七种现有轨迹预测器的性能。实验结果验证了其有效性。代码将在https://github.com/Zhanwei-Z/G2LTraj开源。