Anomaly Detection in Autonomous Driving: A Survey
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866912725229109248 |
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| author | Bogdoll, Daniel Nitsche, Maximilian Zöllner, J. Marius |
| author_facet | Bogdoll, Daniel Nitsche, Maximilian Zöllner, J. Marius |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_07974 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Anomaly Detection in Autonomous Driving: A Survey Bogdoll, Daniel Nitsche, Maximilian Zöllner, J. Marius Robotics 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. |
| title | Anomaly Detection in Autonomous Driving: A Survey |
| topic | Robotics |
| url | https://arxiv.org/abs/2204.07974 |