Safe Perception -- A Hierarchical Monitor Approach
Our transportation world is rapidly transforming induced by an ever increasing level of autonomy. However, to obtain license of fully automated vehicles for widespread public use, it is necessary to assure safety of the entire system, which is still a challenge. This holds in particular for AI-based...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
01.08.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Our transportation world is rapidly transforming induced by an ever
increasing level of autonomy. However, to obtain license of fully automated
vehicles for widespread public use, it is necessary to assure safety of the
entire system, which is still a challenge. This holds in particular for
AI-based perception systems that have to handle a diversity of environmental
conditions and road users, and at the same time should robustly detect all
safety relevant objects (i.e no detection misses should occur). Yet, limited
training and validation data make a proof of fault-free operation hardly
achievable, as the perception system might be exposed to new, yet unknown
objects or conditions on public roads. Hence, new safety approaches for
AI-based perception systems are required. For this reason we propose in this
paper a novel hierarchical monitoring approach that is able to validate the
object list from a primary perception system, can reliably detect detection
misses, and at the same time has a very low false alarm rate. |
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DOI: | 10.48550/arxiv.2208.00824 |