Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by running public benchmarks. While state-of-the-art methods are impressive, i.e., they have no off-road prediction, their generalization to cities outside of the benchmark is unknown. In this work, we show that those methods do not generalize to new scenes. We present a novel method that automatically generates realistic scenes that cause state-of-the-art models go off-road. We frame the problem through the lens of adversarial scene generation. We promote a simple yet effective generative model based on atomic scene generation functions along with physical constraints. Our experiments show that more than $60\%$ of the existing scenes from the current benchmarks can be modified in a way to make prediction methods fail (predicting off-road). We further show that (i) the generated scenes are realistic since they do exist in the real world, and (ii) can be used to make existing models robust by 30-40%. Code is available at https://s-attack.github.io/.

本文研究车辆轨迹预测在自动驾驶汽车中的应用，提出一种对抗性场景生成方法来测试最先进的方法对不同城市场景的普适性，并开发了一种基于场景原子生成功能和物理约束的简单有效生成模型，通过修改生成的场景，使现有模型的错误率降低30-40％。

车辆轨迹预测适用性有限