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：自动驾驶车辆的规模化应用高度依赖其处理异常情况（如道路上的罕见物体）的能力。为应对此类场景，首要任务是实现异常检测。近年来，自动驾驶异常检测虽取得显著进展，但现有基准存在设计缺陷，且过度聚焦于摄像头数据。本文提出AnoVox——迄今为止最大的自动驾驶异常检测基准。AnoVox包含大规模多模态传感器数据及空间体素级真值，可支持不同传感器独立方法的对比。我们给出了正常性的形式化定义，并构建了符合该定义的训练数据集。AnoVox是首个同时包含内容异常与时间异常的基准。