Resource Efficient Classification of Road Conditions through CNN Pruning Article Swipe
Daniel Fink
,
Alexander Busch
,
Mark Wielitzka
,
Tobias Ortmaier
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.913
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2020.12.913
Towards autonomous driving, advanced driver assistance systems increasingly undertake basic driving tasks by replacing human assessment and interactions, when controlling the vehicle. The performance of these systems is directly related to knowledge of the vehicle's state and influential parameters. In this respect, the road condition has a major influence on the tires' traction and thus significantly affects the behavior of the vehicle. Therefore, a prediction of the upcoming road condition can improve the performance of the assistance systems which leads to an increased driving safety and comfort. The presented work aims to classify the road surface as well as its weather-related condition, based on images of the front camera view, using deep convolutional neural networks. In order to take computational limitations of vehicle control units into account, a pruning approach is investigated to reduce the network complexity.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2020.12.913
- OA Status
- diamond
- Cited By
- 8
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3153970996
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3153970996Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2020.12.913Digital Object Identifier
- Title
-
Resource Efficient Classification of Road Conditions through CNN PruningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
-
Daniel Fink, Alexander Busch, Mark Wielitzka, Tobias OrtmaierList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2020.12.913Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ifacol.2020.12.913Direct OA link when available
- Concepts
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Convolutional neural network, Pruning, Computer science, Artificial intelligence, Artificial neural network, Road surface, Traction control system, Advanced driver assistance systems, Machine learning, Automotive engineering, Engineering, Biology, Civil engineering, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5002682219 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.70384438 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |