  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.

Artificial Intelligence