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.
Cite
Text
Bogdoll et al. "AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-91767-7_15Markdown
[Bogdoll et al. "AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/bogdoll2024eccvw-anovox/) doi:10.1007/978-3-031-91767-7_15BibTeX
@inproceedings{bogdoll2024eccvw-anovox,
title = {{AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving}},
author = {Bogdoll, Daniel and Hamdard, Iramm and Rößler, Lukas Namgyu and Geisler, Felix and Bayram, Muhammed and Wang, Felix and Imhof, Jan and de Campos, Miguel and Tabarov, Anushervon and Yang, Yitian and Gontscharow, Martin and Gottschalk, Hanno and Zöllner, J. Marius},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {206-223},
doi = {10.1007/978-3-031-91767-7_15},
url = {https://mlanthology.org/eccvw/2024/bogdoll2024eccvw-anovox/}
}