Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction

2026-03-11

AI Summary

This work addresses the vulnerability of trajectory prediction models to minor adversarial perturbations, which can lead to catastrophic prediction failures. The study presents the first systematic evaluation of robustness in this domain and introduces a lightweight, general-purpose defense mechanism based on randomized smoothing. By integrating this approach with diverse base models across multiple benchmark datasets, the proposed method significantly enhances robustness against adversarial attacks while preserving original prediction accuracy on clean inputs. The results demonstrate that the technique offers an effective and practical solution for improving the safety and reliability of trajectory prediction systems without imposing substantial computational overhead.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate and robust trajectory prediction is essential for safe and efficient autonomous driving, yet recent work has shown that even state-of-the-art prediction models are highly vulnerable to inputs being mildly perturbed by adversarial attacks. Although model vulnerabilities to such attacks have been studied, work on effective countermeasures remains limited. In this work, we develop and evaluate a new defense mechanism for trajectory prediction models based on randomized smoothing -- an approach previously applied successfully in other domains. We evaluate its ability to improve model robustness through a series of experiments that test different strategies of randomized smoothing. We show that our approach can consistently improve prediction robustness of multiple base trajectory prediction models in various datasets without compromising accuracy in non-adversarial settings. Our results demonstrate that randomized smoothing offers a simple and computationally inexpensive technique for mitigating adversarial attacks in trajectory prediction.

Problem

• adversarial attacks
• trajectory prediction
• randomized smoothing
• model robustness
• autonomous driving

Innovation

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