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AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

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arxiv 2405.07865 v4 pith:PSL5JEKN submitted 2024-05-13 cs.CV cs.RO

Daniel Bogdoll , Iramm Hamdard , Lukas Namgyu Rö{ss}ler , Felix Geisler , Muhammed Bayram , Felix Wang , Jan Imhof , Miguel de Campos
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Anushervon Tabarov Yitian Yang Hanno Gottschalk J. Marius Zöllner
This is my paper
classification cs.CVcs.RO
keywords anovoxautonomousanomaliesanomalybenchmarkdetectiondrivingdata
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Anomalous Decision Discovery using Inverse Reinforcement Learning

    cs.AI 2025-07 conditional novelty 6.0 of 10

    TRAP combines trajectory-ranked IRL rewards with variable-horizon prefix sampling and DistilBERT to detect anomalous driving, reaching 0.90 AUC in simulation.

  2. Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Road-query polygons plus CLIP zero-shot filtering turn Mask2Former soft masks into stronger training-free road anomaly segmentations, topping Maskomaly AP on Fishyscapes LostAndFound.

  3. A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording

    cs.CV 2025-07 conditional novelty 4.0 of 10

    An online Mahalanobis-distance novelty filter, updated with streaming data, selects a smaller traffic-sign training set that can outperform the full dataset and random sampling.

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