提出鲁棒的视野估计方法，提升自动驾驶感知安全。
Probabilistic Segmentation for Robust Field of View Estimation
- 基于学习的分割模型结合蒙特卡洛丢弃，量化不确定性。
- 在多种环境下验证，对攻击具有强抗性且泛化能力强。
- 适合实时部署，适用于多场景自动驾驶系统。
感知与认知攻击威胁自动驾驶车辆的安全部署。具备安全意识的传感器融合可缓解此类威胁，但依赖于精确的视场（FOV）估计，而该问题尚未在自主系统中得到充分评估。为此，我们借鉴经典计算机图形学算法，开发首个面向自主系统的FOV估计器，并构建首个带有真实标签的FOV数据集。然而，我们发现这些方法自身也极易受到感知攻击。为提升FOV估计的鲁棒性，我们提出一种基于学习的分割模型，捕捉视场特征，结合蒙特卡洛丢弃（MCD）进行不确定性量化，并在置信度图上执行异常检测。通过全面评估，证明该方法具备良好的抗攻击能力与跨环境泛化性能。架构权衡实验表明，该模型适用于多种应用中的实时部署。
原文摘要 · Abstract (English)
Attacks on sensing and perception threaten the safe deployment of autonomous vehicles (AVs). Security-aware sensor fusion helps mitigate threats but requires accurate field of view (FOV) estimation which has not been evaluated autonomy. To address this gap, we adapt classical computer graphics algorithms to develop the first autonomy-relevant FOV estimators and create the first datasets with ground truth FOV labels. Unfortunately, we find that these approaches are themselves highly vulnerable to attacks on sensing. To improve robustness of FOV estimation against attacks, we propose a learning-based segmentation model that captures FOV features, integrates Monte Carlo dropout (MCD) for uncertainty quantification, and performs anomaly detection on confidence maps. We illustrate through comprehensive evaluations attack resistance and strong generalization across environments. Architecture trade studies demonstrate the model is feasible for real-time deployment in multiple applications.
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