Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, i.e., having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a “Realistic Residual Block” (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes. Code is available at: https://github.com/vita-epfl/RRB.

车辆轨迹预测任务通常从两个不同的角度解决：使用知识驱动的方法，或者最近使用数据驱动的方法。一方面，我们可以明确地实现领域知识或物理先验，例如预期车辆将沿着道路中间行驶。虽然这种观点导致可行的输出，但是由于在城市环境中难以进行复杂的交互操作而使其性能有限。另一方面，最近的工作使用了数据驱动的方法，该方法可以从数据中学习复杂的交互作用，从而获得卓越的性能。但是，泛化，即对看不见的数据进行准确的预测是导致输出不切实际的问题。在本文中，我们建议学习一种“现实残差块”（RRB），它可以有效地将这两种观点联系起来。我们的RRB采用任何现成的知识驱动模型，并找到所需的残差以添加到知识感知轨迹中。我们提出的方法通过限制残差范围并考虑其不确定性来输出现实的预测。我们还使用模型预测控制（MPC）来约束输出，以满足运动学上的约束。使用公开可用的数据集，我们表明，在准确性和对新场景的泛化方面，我们的方法优于以前的工作。可以从以下网址获得代码：https：//github.com/vita-epfl/RRB。