Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification Article Swipe
Friedrich Kruber
,
Jonas Wurst
,
Eduardo Sánchez Morales
,
Samarjit Chakraborty
,
Michael Botsch
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.02126
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.02126
The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopic traffic simulation, a clustering technique and a classification technique for the operational phase. The developed simulation tool models each vehicle separately, while maintaining the dependencies between each other. The clustering approach consists of a modified unsupervised Random Forest algorithm to find a data adaptive similarity measure between all scenarios. As part of this, the path proximity, a novel technique to determine a similarity based on the Random Forest algorithm is presented. In the second part of the clustering, the similarities are used to define a set of clusters. In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase. A thresholding technique is described to ensure a certain confidence level for the class assignment. The method is applied for highway scenarios. The results show that the proposed method is an excellent approach to automatically categorize traffic scenarios, which is particularly relevant for testing autonomous vehicle functionality.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.48550/arxiv.2004.02126
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2983248310
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2983248310Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.02126Digital Object Identifier
- Title
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Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
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2020-04-05Full publication date if available
- Authors
-
Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael BotschList of authors in order
- Landing page
-
https://doi.org/10.48550/arxiv.2004.02126Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48550/arxiv.2004.02126Direct OA link when available
- Concepts
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Cluster analysis, Random forest, Computer science, Data mining, Artificial intelligence, Thresholding, Classifier (UML), Machine learning, Categorization, Unsupervised learning, Set (abstract data type), Pattern recognition (psychology), Programming language, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.able | 12 |
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| abstract_inverted_index.goal | 1 |
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| abstract_inverted_index.path | 83 |
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| abstract_inverted_index.novel | 86 |
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| abstract_inverted_index.which | 10, 173 |
| abstract_inverted_index.while | 50 |
| abstract_inverted_index.Forest | 66, 96, 123 |
| abstract_inverted_index.Random | 65, 95, 122 |
| abstract_inverted_index.define | 112 |
| abstract_inverted_index.ensure | 141 |
| abstract_inverted_index.method | 151, 163 |
| abstract_inverted_index.models | 46 |
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| abstract_inverted_index.phase. | 41, 134 |
| abstract_inverted_index.second | 102 |
| abstract_inverted_index.applied | 153 |
| abstract_inverted_index.between | 54, 75 |
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| abstract_inverted_index.highway | 155 |
| abstract_inverted_index.measure | 74 |
| abstract_inverted_index.method, | 9 |
| abstract_inverted_index.provide | 7 |
| abstract_inverted_index.results | 158 |
| abstract_inverted_index.testing | 178 |
| abstract_inverted_index.traffic | 17, 29, 171 |
| abstract_inverted_index.trained | 126 |
| abstract_inverted_index.vehicle | 48, 180 |
| abstract_inverted_index.adaptive | 72 |
| abstract_inverted_index.approach | 59, 167 |
| abstract_inverted_index.clusters | 130 |
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| abstract_inverted_index.relevant | 176 |
| abstract_inverted_index.algorithm | 67, 97 |
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| abstract_inverted_index.determine | 89 |
| abstract_inverted_index.developed | 43 |
| abstract_inverted_index.excellent | 166 |
| abstract_inverted_index.scenarios | 18 |
| abstract_inverted_index.technique | 33, 37, 87, 137 |
| abstract_inverted_index.autonomous | 179 |
| abstract_inverted_index.categories | 15 |
| abstract_inverted_index.categorize | 170 |
| abstract_inverted_index.classifier | 124 |
| abstract_inverted_index.clustering | 32, 58 |
| abstract_inverted_index.confidence | 144 |
| abstract_inverted_index.presented. | 99 |
| abstract_inverted_index.proximity, | 84 |
| abstract_inverted_index.scenarios, | 172 |
| abstract_inverted_index.scenarios. | 77, 156 |
| abstract_inverted_index.similarity | 73, 91 |
| abstract_inverted_index.simulation | 44 |
| abstract_inverted_index.assignment. | 149 |
| abstract_inverted_index.clustering, | 106 |
| abstract_inverted_index.components: | 26 |
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| abstract_inverted_index.similarities | 108 |
| abstract_inverted_index.thresholding | 136 |
| abstract_inverted_index.unsupervised | 64 |
| abstract_inverted_index.automatically | 169 |
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| abstract_inverted_index.classification | 36 |
| abstract_inverted_index.functionality. | 181 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.45287178 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |