Anomaly Detection in Autonomous Driving: A Survey

Fuente: arXiv
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Auteurs principaux: Bogdoll, Daniel, Nitsche, Maximilian, Zöllner, J. Marius
Format: Preprint
Publié: 2022
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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