Trajectory prediction plays an essential role in autonomous vehicles. While numerous strategies have been developed to enhance the robustness of trajectory prediction models, these methods are predominantly heuristic and do not offer guaranteed robustness against adversarial attacks and noisy observations. In this work, we propose a certification approach tailored for the task of trajectory prediction. To this end, we address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality, resulting in a model that provides guaranteed robustness. Furthermore, we integrate a denoiser into our method to further improve the performance. Through comprehensive evaluations, we demonstrate the effectiveness of the proposed technique across various baselines and using standard trajectory prediction datasets. The code will be made available online: https://s-attack.github.io/

翻译：轨迹预测在自动驾驶中扮演着关键角色。尽管已有多种策略旨在增强轨迹预测模型的鲁棒性，但这些方法主要基于启发式思路，无法提供对对抗性攻击和噪声观测的保证性鲁棒性。在本工作中，我们提出了一种专为轨迹预测任务设计的认证方法。为此，我们解决了轨迹预测中固有的挑战，包括无界输出和多模态特性，从而构建了一个提供保证性鲁棒性的模型。此外，我们在方法中集成了一个去噪器以进一步提升性能。通过全面评估，我们展示了所提技术在不同基线和标准轨迹预测数据集上的有效性。相关代码将在线公开：https://s-attack.github.io/