Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles. Although previous works have studied adversarial robustness in the context of trajectory forecasting, some significant issues remain unaddressed. In this work, we try to tackle these crucial problems. Firstly, the previous definitions of robustness in trajectory prediction are ambiguous. We thus provide formal definitions for two kinds of robustness, namely label robustness and pure robustness. Secondly, as previous works fail to consider robustness about all points in a disturbance interval, we utilise a probably approximately correct (PAC) framework for robustness verification. Additionally, this framework can not only identify potential counterexamples, but also provides interpretable analyses of the original methods. Our approach is applied using a prototype tool named TrajPAC. With TrajPAC, we evaluate the robustness of four state-of-the-art trajectory prediction models -- Trajectron++, MemoNet, AgentFormer, and MID -- on trajectories from five scenes of the ETH/UCY dataset and scenes of the Stanford Drone Dataset. Using our framework, we also experimentally study various factors that could influence robustness performance.

翻译：鲁棒的行人轨迹预测对于发展安全的自动驾驶车辆至关重要。尽管已有研究在轨迹预测背景下探讨了对抗鲁棒性，但一些关键问题仍未得到解决。本研究尝试解决这些重要问题。首先，先前轨迹预测中鲁棒性的定义较为模糊。为此，我们为两类鲁棒性提供了正式定义，即标签鲁棒性和纯鲁棒性。其次，由于先前工作未能考虑扰动区间内所有点的鲁棒性，我们采用了一个可能近似正确（PAC）框架来进行鲁棒性验证。此外，该框架不仅能识别潜在的反例，还能对原始方法提供可解释的分析。我们的方法通过一个名为TrajPAC的原型工具实现。利用TrajPAC，我们评估了四种最先进的轨迹预测模型（Trajectron++、MemoNet、AgentFormer和MID）在ETH/UCY数据集五个场景以及斯坦福无人机数据集场景上的鲁棒性。基于该框架，我们还通过实验研究了可能影响鲁棒性性能的多种因素。