Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-Based Fault Detection and Identification (Abstract Reprint) Article Swipe
Pasquale Antonante
,
Heath Nilsen
,
Luca Carlone
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i20.30592
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i20.30592
This paper investigates runtime monitoring of perception systems. Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies requires the development of methodologies to guarantee and monitor safe operation. Despite the paramount importance of perception, currently there is no formal approach for system-level perception monitoring. In this paper, we formalize the problem of runtime fault detection and identification in perception systems and present a framework to model diagnostic information using a diagnostic graph. We then provide a set of deterministic, probabilistic, and learning-based algorithms that use diagnostic graphs to perform fault detection and identification. Moreover, we investigate fundamental limits and provide deterministic and probabilistic guarantees on the fault detection and identification results. We conclude the paper with an extensive experimental evaluation, which recreates several realistic failure modes in the LGSVL open-source autonomous driving simulator, and applies the proposed system monitors to a state-of-the-art autonomous driving software stack (Baidu's Apollo Auto). The results show that the proposed system monitors outperform baselines, have the potential of preventing accidents in realistic autonomous driving scenarios, and incur a negligible computational overhead.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i20.30592
- https://ojs.aaai.org/index.php/AAAI/article/download/30592/32754
- OA Status
- diamond
- References
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393157511
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https://openalex.org/W4393157511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v38i20.30592Digital Object Identifier
- Title
-
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-Based Fault Detection and Identification (Abstract Reprint)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-24Full publication date if available
- Authors
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Pasquale Antonante, Heath Nilsen, Luca CarloneList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v38i20.30592Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/30592/32754Direct link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/30592/32754Direct OA link when available
- Concepts
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Reprint, Identification (biology), Probabilistic logic, Perception, Fault detection and isolation, Computer science, Fault (geology), Artificial intelligence, Machine learning, Psychology, Biology, Neuroscience, Astronomy, Paleontology, Physics, Actuator, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.potential | 189 |
| abstract_inverted_index.realistic | 151, 194 |
| abstract_inverted_index.recreates | 149 |
| abstract_inverted_index.Perception | 8 |
| abstract_inverted_index.algorithms | 110 |
| abstract_inverted_index.autonomous | 19, 158, 170, 195 |
| abstract_inverted_index.baselines, | 186 |
| abstract_inverted_index.diagnostic | 94, 98, 113 |
| abstract_inverted_index.guarantees | 131 |
| abstract_inverted_index.importance | 59 |
| abstract_inverted_index.monitoring | 4 |
| abstract_inverted_index.negligible | 201 |
| abstract_inverted_index.operation. | 55 |
| abstract_inverted_index.outperform | 185 |
| abstract_inverted_index.perception | 6, 30, 70, 86 |
| abstract_inverted_index.preventing | 191 |
| abstract_inverted_index.scenarios, | 197 |
| abstract_inverted_index.simulator, | 160 |
| abstract_inverted_index.development | 47 |
| abstract_inverted_index.evaluation, | 147 |
| abstract_inverted_index.fundamental | 124 |
| abstract_inverted_index.information | 95 |
| abstract_inverted_index.investigate | 123 |
| abstract_inverted_index.monitoring. | 71 |
| abstract_inverted_index.open-source | 157 |
| abstract_inverted_index.perception, | 61 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.experimental | 146 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.self-driving | 23 |
| abstract_inverted_index.system-level | 69 |
| abstract_inverted_index.technologies | 44 |
| abstract_inverted_index.applications, | 27 |
| abstract_inverted_index.computational | 202 |
| abstract_inverted_index.deterministic | 128 |
| abstract_inverted_index.methodologies | 49 |
| abstract_inverted_index.probabilistic | 130 |
| abstract_inverted_index.deterministic, | 106 |
| abstract_inverted_index.high-integrity | 14 |
| abstract_inverted_index.identification | 84, 137 |
| abstract_inverted_index.learning-based | 109 |
| abstract_inverted_index.probabilistic, | 107 |
| abstract_inverted_index.identification. | 120 |
| abstract_inverted_index.state-of-the-art | 169 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.07309942 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |