In this letter, we propose a novel approach for agent motion prediction in cluttered environments. One of the main challenges in predicting agent motion is accounting for location and context-specific information. Our main contribution is the concept of learning context maps to improve the prediction task. Context maps are a set of location-specific latent maps that are trained alongside the predictor. Thus, the proposed maps are capable of capturing location context beyond visual context cues (e.g. usual average speeds and typical trajectories) or predefined map primitives (such as lanes and stop lines). We pose context map learning as a multi-task training problem and describe our map model and its incorporation into a state-of-the-art trajectory predictor. In extensive experiments, it is shown that use of learned maps can significantly improve predictor accuracy. Furthermore, the performance can be additionally boosted by providing partial knowledge of map semantics.

中文翻译：

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深度上下文地图：使用特定于位置的潜在地图的代理轨迹预测

在这封信中，我们提出了一种在杂乱环境中进行代理运动预测的新方法。预测代理运动的主要挑战之一是考虑位置和特定于上下文的信息。我们的主要贡献是学习上下文图以改进预测任务的概念。上下文地图是一组与预测器一起训练的特定于位置的潜在地图。因此，所提出的地图能够捕捉视觉上下文线索（例如通常的平均速度和典型轨迹）或预定义的地图原语（例如车道和停止线）之外的位置上下文。我们将上下文地图学习视为一个多任务训练问题，并描述了我们的地图模型及其与最先进的轨迹预测器的结合。在广泛的实验中，结果表明，使用学习地图可以显着提高预测器的准确性。此外，还可以通过提供部分地图语义知识来进一步提高性能。