Our transportation field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustness has not been carefully studied. In this paper, we introduce a socially-attended attack to assess the social understanding of prediction models in terms of collision avoidance. An attack is a small yet carefully-crafted perturbations to fail predictors. Technically, we define collision as a failure mode of the output, and propose hard- and soft-attention mechanisms to guide our attack. Thanks to our attack, we shed light on the limitations of the current models in terms of their social understanding. We demonstrate the strengths of our method on the recent trajectory prediction models. Finally, we show that our attack can be employed to increase the social understanding of state-of-the-art models. The code is available at https://s-attack.github.io/.

我们的运输领域最近见证了基于神经网络的轨迹预测器的军备竞赛。虽然这些预测器是自主导航或行人流模拟等许多应用的核心，但它们的对抗鲁棒性尚未得到仔细研究。在本文中，我们介绍了一种社会参与攻击，以评估预测模型在避免碰撞方面的社会理解。攻击是对预测器失败的小而精心设计的扰动。从技术上讲，我们将碰撞定义为输出的失败模式，并提出硬注意力和软注意力机制来指导我们的攻击。由于我们的攻击，我们阐明了当前模型在社会理解方面的局限性。我们展示了我们的方法在最近的轨迹预测模型上的优势。最后，我们表明，我们的攻击可以用来增加对最先进模型的社会理解。该代码可在 https://s-attack.github.io/ 获得。