AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

Abstract

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.

Daniel Bogdoll, Iramm Hamdard, and Lukas Namgyu Rößler contributed equally. Accepted for publication at ECCV 2024 W-CODA workshop

References in corpus (9)

• DINOv2: Learning Robust Visual Features without Supervision
• Rain rendering for evaluating and improving robustness to bad weather
• SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
• Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds
• One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
• Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
• Ad-datasets: a meta-collection of data sets for autonomous driving
• Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
• MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

Cited by in corpus (1)

• Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities