评估随机平滑作为对抗攻击在轨迹预测中的防御手段
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
AI总结：
本文提出基于随机平滑的轨迹预测防御方法，通过实验验证其在提升模型鲁棒性的同时保持预测准确性。
摘要：
准确且鲁棒的轨迹预测对于安全高效的自动驾驶至关重要，然而近期研究表明，即使最先进的预测模型也对由对抗攻击轻微扰动的输入高度敏感。尽管已研究了模型对这类攻击的脆弱性，但有效对策的研究仍有限。在本工作中，我们开发并评估了一种基于随机平滑的新防御机制，这是一种在其他领域成功应用的方法。我们通过一系列实验测试了不同随机平滑策略，以评估其提高模型鲁棒性的能力。我们证明，我们的方法能一致地提升多种基础轨迹预测模型在不同数据集中的预测鲁棒性，且在非对抗环境下不牺牲准确性。我们的结果表明，随机平滑提供了一种简单且计算成本低的缓解轨迹预测中对抗攻击的技术。
英文摘要：
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.
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