Certified Human Trajectory Prediction
Mohammadhossein Bahari Saeed Saadatnejad Amirhossein Asgari Farsangi Seyed-Mohsen Moosavi-Dezfooli Alexandre Alahi
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/