The reliability of a machine vision system for autonomous driving depends heavily on its training data distribution. When a vehicle encounters significantly different conditions, such as atypical obstacles, its perceptual capabilities can degrade substantially. Unlike many domains where errors carry limited consequences, failures in autonomous driving translate directly into physical risk for passengers, pedestrians, and other road users. To address this challenge, we explore Visual Anomaly Detection (VAD) as a solution. VAD enables the identification of anomalous objects not present during training, allowing the system to alert the driver when an unfamiliar situation is detected. Crucially, VAD models produce pixel-level anomaly maps that can guide driver attention to specific regions of concern without requiring any prior assumptions about the nature or form of the hazard. We benchmark eight state-of-the-art VAD methods on AnoVox, the largest synthetic dataset for anomaly detection in autonomous driving. In particular, we evaluate performance across four backbone architectures spanning from large networks to lightweight ones such as MobileNet and DeiT-Tiny. Our results demonstrate that VAD transfers effectively to road scenes. Notably, Tiny-Dinomaly achieves the best accuracy-efficiency trade-off for edge deployment, matching full-scale localization performance at a fraction of the memory cost. This study represents a concrete step toward safer, more responsible deployment of autonomous vehicles, ultimately improving protection for passengers, pedestrians, and all road users.

翻译：自动驾驶机器视觉系统的可靠性高度依赖于其训练数据分布。当车辆遭遇显著不同的条件（如非典型障碍物）时，其感知能力可能出现严重退化。与许多错误后果有限的领域不同，自动驾驶中的故障直接转化为乘客、行人及其他道路使用者的人身安全风险。针对这一挑战，我们探索了视觉异常检测（VAD）作为解决方案。VAD能够识别训练阶段未出现的异常物体，使系统在检测到陌生情境时向驾驶员发出警报。关键的是，VAD模型可生成像素级异常图，无需对危险的性质或形态预设任何先验假设，即可引导驾驶员关注特定风险区域。我们基于AnoVox（自动驾驶异常检测领域最大的合成数据集）对八种先进VAD方法进行了基准测试。特别地，我们评估了从大型网络到轻量级网络（如MobileNet和DeiT-Tiny）的四种骨干架构的性能表现。结果表明，VAD可有效迁移至道路场景。值得注意的是，Tiny-Dinomaly在边缘部署场景中实现了最佳的精度-效率平衡，以极低的存储开销达到了全尺寸模型的定位性能。本研究为更安全、更负责任的自动驾驶车辆部署迈出了实质性一步，最终将提升对乘客、行人及所有道路使用者的保护水平。