The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong focus on camera data. In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date. AnoVox incorporates large-scale multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. We propose a formal definition of normality and provide a compliant training dataset. AnoVox is the first benchmark to contain both content and temporal anomalies.

翻译：自动驾驶汽车的规模化部署高度依赖于其处理异常情况（如道路上的罕见物体）的能力。为应对此类情形，首要任务是有效检测异常。近年来，自动驾驶异常检测领域取得了显著进展，但现有基准设计存在不足，且过度聚焦于摄像头数据。为此，我们提出AnoVox，这是迄今规模最大的自动驾驶异常检测基准。AnoVox融合了大规模多模态传感器数据与空间体素（VOXel）真值，支持独立于传感器类型的方法对比。我们提出了正常性的规范定义，并构建了符合该定义的训练数据集。AnoVox是首个同时涵盖内容异常与时间异常的基准。