Motion forecasting plays a critical role in enabling robots to anticipate future trajectories of surrounding agents and plan accordingly. However, existing forecasting methods often rely on curated datasets that are not faithful to what real-world perception pipelines can provide. In reality, upstream modules that are responsible for detecting and tracking agents, and those that gather road information to build the map, can introduce various errors, including misdetections, tracking errors, and difficulties in being accurate for distant agents and road elements. This paper aims to uncover the challenges of bringing motion forecasting models to this more realistic setting where inputs are provided by perception modules. In particular, we quantify the impacts of the domain gap through extensive evaluation. Furthermore, we design synthetic perturbations to better characterize their consequences, thus providing insights into areas that require improvement in upstream perception modules and guidance toward the development of more robust forecasting methods.

翻译：运动预测在使机器人能够预测周围智能体的未来轨迹并据此规划路径方面发挥着关键作用。然而，现有的预测方法通常依赖于经过精心处理的数据集，这些数据集并未忠实反映真实世界感知流水线的输入情况。实际上，负责检测与跟踪智能体的上游模块，以及负责采集道路信息以构建地图的模块，可能会引入各种误差，包括漏检、跟踪错误，以及在远距离智能体和道路元素上难以保持准确性等问题。本文旨在揭示将运动预测模型应用于这一更现实的场景（即输入由感知模块提供）时所面临的挑战。具体而言，我们通过广泛评估量化了领域差距的影响。此外，我们设计了合成扰动以更好地表征其后果，从而为上游感知模块需要改进的领域提供见解，并为开发更鲁棒的预测方法提供指导。