TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
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 smo...
| Authors | Liang Zhang, Gaojie Jin, Yao Shi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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