Miniature Autonomy as One Important Testing Means in the Development of Machine Learning Methods for Autonomous Driving: How ML-based Autonomous Driving could be Realized on a 1:87 Scale Article Swipe
Tim Tiedemann
,
Jonas Fuhrmann
,
Sebastian Paulsen
,
Thorben Schnirpel
,
Nils Schönherr
,
Bettina Buth
,
Stephan Pareigis
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5220/0007955704830488
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5220/0007955704830488
In the current state of autonomous driving machine learning methods are dominating, especially for the environment recognition. For such solutions, the reliability and the robustness is a critical question. A “miniature autonomy” with model vehicles at a small scale could be beneficial for different reasons. Examples are (1) the testability of dangerous and close-to-crash edge cases, (2) the possibility to test potentially dangerous concepts as end-to-end learning or combined inference and learning phases, (3) the need to optimize algorithms thoroughly, and (4) a potential reduction of test mile counts. Presented is the motivation for miniature autonomy and a discussion of testing of machine learning methods. Finally, two currently set up platforms including one with an FPGA-based TPU for ML acceleration are described.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0007955704830488
- OA Status
- gold
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2968926718
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2968926718Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5220/0007955704830488Digital Object Identifier
- Title
-
Miniature Autonomy as One Important Testing Means in the Development of Machine Learning Methods for Autonomous Driving: How ML-based Autonomous Driving could be Realized on a 1:87 ScaleWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Tim Tiedemann, Jonas Fuhrmann, Sebastian Paulsen, Thorben Schnirpel, Nils Schönherr, Bettina Buth, Stephan PareigisList of authors in order
- Landing page
-
https://doi.org/10.5220/0007955704830488Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5220/0007955704830488Direct OA link when available
- Concepts
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Robustness (evolution), Computer science, Machine learning, Artificial intelligence, Testability, Inference, Crash, Test set, Embedded system, Reliability engineering, Engineering, Chemistry, Biochemistry, Gene, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2021: 1, 2020: 1, 2019: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.are | 10, 46, 120 |
| abstract_inverted_index.for | 13, 42, 93, 117 |
| abstract_inverted_index.one | 112 |
| abstract_inverted_index.set | 108 |
| abstract_inverted_index.the | 1, 14, 20, 23, 48, 57, 74, 91 |
| abstract_inverted_index.two | 106 |
| abstract_inverted_index.edge | 54 |
| abstract_inverted_index.mile | 87 |
| abstract_inverted_index.need | 75 |
| abstract_inverted_index.such | 18 |
| abstract_inverted_index.test | 60, 86 |
| abstract_inverted_index.with | 32, 113 |
| abstract_inverted_index.could | 39 |
| abstract_inverted_index.model | 33 |
| abstract_inverted_index.scale | 38 |
| abstract_inverted_index.small | 37 |
| abstract_inverted_index.state | 3 |
| abstract_inverted_index.cases, | 55 |
| abstract_inverted_index.counts. | 88 |
| abstract_inverted_index.current | 2 |
| abstract_inverted_index.driving | 6 |
| abstract_inverted_index.machine | 7, 102 |
| abstract_inverted_index.methods | 9 |
| abstract_inverted_index.phases, | 72 |
| abstract_inverted_index.testing | 100 |
| abstract_inverted_index.Examples | 45 |
| abstract_inverted_index.Finally, | 105 |
| abstract_inverted_index.autonomy | 95 |
| abstract_inverted_index.combined | 68 |
| abstract_inverted_index.concepts | 63 |
| abstract_inverted_index.critical | 27 |
| abstract_inverted_index.learning | 8, 66, 71, 103 |
| abstract_inverted_index.methods. | 104 |
| abstract_inverted_index.optimize | 77 |
| abstract_inverted_index.reasons. | 44 |
| abstract_inverted_index.vehicles | 34 |
| abstract_inverted_index.Presented | 89 |
| abstract_inverted_index.currently | 107 |
| abstract_inverted_index.dangerous | 51, 62 |
| abstract_inverted_index.different | 43 |
| abstract_inverted_index.including | 111 |
| abstract_inverted_index.inference | 69 |
| abstract_inverted_index.miniature | 94 |
| abstract_inverted_index.platforms | 110 |
| abstract_inverted_index.potential | 83 |
| abstract_inverted_index.question. | 28 |
| abstract_inverted_index.reduction | 84 |
| abstract_inverted_index.FPGA-based | 115 |
| abstract_inverted_index.algorithms | 78 |
| abstract_inverted_index.autonomous | 5 |
| abstract_inverted_index.beneficial | 41 |
| abstract_inverted_index.described. | 121 |
| abstract_inverted_index.discussion | 98 |
| abstract_inverted_index.end-to-end | 65 |
| abstract_inverted_index.especially | 12 |
| abstract_inverted_index.motivation | 92 |
| abstract_inverted_index.robustness | 24 |
| abstract_inverted_index.solutions, | 19 |
| abstract_inverted_index.autonomy” | 31 |
| abstract_inverted_index.dominating, | 11 |
| abstract_inverted_index.environment | 15 |
| abstract_inverted_index.possibility | 58 |
| abstract_inverted_index.potentially | 61 |
| abstract_inverted_index.reliability | 21 |
| abstract_inverted_index.testability | 49 |
| abstract_inverted_index.thoroughly, | 79 |
| abstract_inverted_index.acceleration | 119 |
| abstract_inverted_index.recognition. | 16 |
| abstract_inverted_index.“miniature | 30 |
| abstract_inverted_index.close-to-crash | 53 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.71566909 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |