AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

Autor:	Bogdoll, Daniel, Hamdard, Iramm, Rößler, Lukas Namgyu, Geisler, Felix, Bayram, Muhammed, Wang, Felix, Imhof, Jan, de Campos, Miguel, Tabarov, Anushervon, Yang, Yitian, Gottschalk, Hanno, Zöllner, J. Marius
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Daniel Bogdoll, Iramm Hamdard, and Lukas Namgyu R\"o{\ss}ler contributed equally. Accepted for publication at ECCV 2024 W-CODA workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.07865 Zobrazit plný text záznamu View this record from Arxiv