TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction

2026-06-26

AI Summary

Existing pedestrian trajectory prediction models are vulnerable to adversarial perturbations and lack verifiable safety guarantees. This work proposes TrajRS, the first framework to formally define and achieve certifiable robustness for trajectory prediction—covering both the “best prediction” and the entire set of “all possible predictions.” By extending the randomized smoothing framework and explicitly incorporating the temporal and spatial characteristics inherent in trajectory data, TrajRS introduces tailored smoothing and certification mechanisms. Experimental results demonstrate that TrajRS provides effective certified robustness radii for a variety of smoothed trajectory predictors, ensuring reliable predictions even under adversarial perturbations.

Abstract

The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction can significantly impair the accuracy of predicted trajectories, leading to hazardous driving behaviors. While heuristic defense strategies have been implemented to enhance the robustness of trajectory prediction models, these measures often fail against more sophisticated, targeted adversarial attacks. Hence, there is a pressing need to establish verifiable safety assurances for trajectory prediction models. In this paper, we extend the traditional Randomized Smoothing framework to "TrajRS", which provides a certified robust radius for smoothed trajectory predictors. We clarify and expand the formal definitions of robustness in trajectory prediction and tailor the practical TrajRS scheme specifically to "robustness for the optimal prediction" and "robustness for all possible predictions". An extensive set of experiments demonstrates that TrajRS effectively achieves robustness certification for all smoothed pedestrian trajectory predictors in this work.

Problem

• trajectory prediction
• adversarial attacks
• certified robustness
• autonomous driving
• pedestrian safety

Innovation

• certified robustness
• trajectory prediction
• randomized smoothing
• adversarial defense
• autonomous driving