Predicting Autonomous Vehicle Navigation Parameters via Image and Image-and-Point Cloud Fusion-based End-to-End Methods Article Swipe
Semih Beyçimen
,
Dmitry Ignatyev
,
Argyrios Zolotas
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/mfi55806.2022.9913844
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/mfi55806.2022.9913844
This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/mfi55806.2022.9913844
- OA Status
- green
- Cited By
- 2
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306147889
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306147889Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/mfi55806.2022.9913844Digital Object Identifier
- Title
-
Predicting Autonomous Vehicle Navigation Parameters via Image and Image-and-Point Cloud Fusion-based End-to-End MethodsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-09-20Full publication date if available
- Authors
-
Semih Beyçimen, Dmitry Ignatyev, Argyrios ZolotasList of authors in order
- Landing page
-
https://doi.org/10.1109/mfi55806.2022.9913844Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://dspace.lib.cranfield.ac.uk/handle/1826/18794Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Computer vision, End-to-end principle, Point cloud, Lidar, Image (mathematics), Image segmentation, Noise (video), Image processing, Heading (navigation), Image fusion, Remote sensing, Engineering, Geography, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.results | 95 |
| abstract_inverted_index.tested. | 63 |
| abstract_inverted_index.trained | 24, 86 |
| abstract_inverted_index.vehicle | 11, 41 |
| abstract_inverted_index.accuracy | 96 |
| abstract_inverted_index.learning | 27, 37 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.rigorous | 69 |
| abstract_inverted_index.selected | 72 |
| abstract_inverted_index.semantic | 54 |
| abstract_inverted_index.steering | 99 |
| abstract_inverted_index.cropping, | 51 |
| abstract_inverted_index.framework | 76 |
| abstract_inverted_index.rate-wise | 97 |
| abstract_inverted_index.removing, | 53 |
| abstract_inverted_index.Resnet-152 | 33 |
| abstract_inverted_index.algorithms | 48 |
| abstract_inverted_index.autonomous | 10, 40 |
| abstract_inverted_index.end-to-end | 6, 20 |
| abstract_inverted_index.navigation | 12 |
| abstract_inverted_index.parameters | 38 |
| abstract_inverted_index.predicting | 9 |
| abstract_inverted_index.Image-based | 14 |
| abstract_inverted_index.navigation, | 42 |
| abstract_inverted_index.parameters. | 13 |
| abstract_inverted_index.architecture | 85 |
| abstract_inverted_index.investigated | 61 |
| abstract_inverted_index.points-based | 19, 90 |
| abstract_inverted_index.segmentation | 55 |
| abstract_inverted_index.Densenet-169, | 32 |
| abstract_inverted_index.Inception-v4. | 35 |
| abstract_inverted_index.architectures | 28 |
| abstract_inverted_index.investigation, | 70 |
| abstract_inverted_index.pre-processing | 46 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.45960392 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |