Anomaly Detection in Autonomous Driving: A Survey

Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner

CVPRW 2022 pp. 4487-4498

doi:10.1109/CVPRW56347.2022.00495 /cvprw/2022/bogdoll2022cvprw-anomaly/

Abstract

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

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Cite

Text

Bogdoll et al. "Anomaly Detection in Autonomous Driving: A Survey." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00495

Markdown

[Bogdoll et al. "Anomaly Detection in Autonomous Driving: A Survey." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/bogdoll2022cvprw-anomaly/) doi:10.1109/CVPRW56347.2022.00495

BibTeX

@inproceedings{bogdoll2022cvprw-anomaly,
  title     = {{Anomaly Detection in Autonomous Driving: A Survey}},
  author    = {Bogdoll, Daniel and Nitsche, Maximilian and Zöllner, J. Marius},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {4487-4498},
  doi       = {10.1109/CVPRW56347.2022.00495},
  url       = {https://mlanthology.org/cvprw/2022/bogdoll2022cvprw-anomaly/}
}