Predicting the future trajectories of pedestrians is a challenging problem
that has a range of application, from crowd surveillance to autonomous driving.
In literature, methods to approach pedestrian trajectory prediction have
evolved, transitioning from physics-based models to data-driven models based on
recurrent neural networks. In this work, we propose a new approach to
pedestrian trajectory prediction, with the introduction of a novel 2D
convolutional model. This new model outperforms recurrent models, and it
achieves state-of-the-art results on the ETH and TrajNet datasets. We also
present an effective system to represent pedestrian positions and powerful data
augmentation techniques, such as the addition of Gaussian noise and the use of
random rotations, which can be applied to any model. As an additional
exploratory analysis, we present experimental results on the inclusion of
occupancy methods to model social information, which empirically show that
these methods are ineffective in capturing social interaction.

本文提出了一种新的基于 2D 卷积模型的行人轨迹预测方法，采用了高效的数据表示和强大的数据增强技术，在 ETH 和 TrajNet 数据集上实现了最优结果。另外，对于以前用于建模社交信息的占据方法进行了实验性探究，并得出这些方法无法捕捉社交交互的经验结论。