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：自动驾驶技术的规模化应用在很大程度上取决于其处理异常情况的能力，例如道路上出现的罕见物体。为了应对此类情况，首先必须检测到异常。过去几年，自动驾驶领域的异常检测取得了巨大进展，但现有基准测试设计欠佳，且过度依赖摄像头数据。在本研究中，我们提出了AnoVox，这是迄今为止规模最大的自动驾驶异常检测基准。AnoVox整合了大规模多模态传感器数据与空间体素（VOXel）真实标注，使得不同传感器方案的方法能够进行公平比较。我们提出了规范性的形式化定义，并提供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测试。