3D Roadway Scene Object Detection with LiDARs in Snowfall Conditions Article Swipe
Ghazal Farhani
,
Taufiq Rahman
,
Syed Mostaquim Ali
,
Andrew Liu
,
Mohamed I. Zaki
,
Dominique Charlebois
,
Benoit Anctil
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/itsc58415.2024.10920127
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/itsc58415.2024.10920127
Because 3D structure of a roadway environment can be characterized directly by a Light Detection and Ranging (LiDAR) sensors, they can be used to obtain exceptional situational awareness for assitive and autonomous driving systems. Although LiDARs demonstrate good performance in clean and clear weather conditions, their performance significantly deteriorates in adverse weather conditions such as those involving atmospheric precipitation. This may render perception capabilities of autonomous systems that use LiDAR data in learning based models to perform object detection and ranging ineffective. While efforts have been made to enhance the accuracy of these models, the extent of signal degradation under various weather conditions remains largely not quantified. In this study, we focus on the performance of an automotive grade LiDAR in snowy conditions in order to develop a physics-based model that examines failure modes of a LiDAR sensor. Specifically, we investigated how the LiDAR signal attenuates with different snowfall rates and how snow particles near the source serve as small but efficient reflectors. Utilizing our model, we transform data from clear conditions to simulate snowy scenarios, enabling a comparison of our synthetic data with actual snowy conditions. Furthermore, we employ this synthetic data, representative of different snowfall rates, to explore the impact on a pre-trained object detection model, assessing its performance under varying levels of snowfall
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/itsc58415.2024.10920127
- OA Status
- green
- References
- 23
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408696999Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/itsc58415.2024.10920127Digital Object Identifier
- Title
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3D Roadway Scene Object Detection with LiDARs in Snowfall ConditionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-24Full publication date if available
- Authors
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Ghazal Farhani, Taufiq Rahman, Syed Mostaquim Ali, Andrew Liu, Mohamed I. Zaki, Dominique Charlebois, Benoit AnctilList of authors in order
- Landing page
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https://doi.org/10.1109/itsc58415.2024.10920127Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2510.22436Direct OA link when available
- Concepts
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Lidar, Snow, Object detection, Computer science, Computer vision, Artificial intelligence, Object (grammar), Remote sensing, Pattern recognition (psychology), Geography, MeteorologyTop 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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| primary_location.raw_source_name | 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) |
| primary_location.landing_page_url | https://doi.org/10.1109/itsc58415.2024.10920127 |
| publication_date | 2024-09-24 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2905253977, https://openalex.org/W2094658656, https://openalex.org/W2510506585, https://openalex.org/W2908808036, https://openalex.org/W2897876743, https://openalex.org/W2563763605, https://openalex.org/W2769526624, https://openalex.org/W3187263517, https://openalex.org/W4313162344, https://openalex.org/W4408352647, https://openalex.org/W2955181123, https://openalex.org/W3088216226, https://openalex.org/W39179224, https://openalex.org/W6776561429, https://openalex.org/W6779237553, https://openalex.org/W2123010717, https://openalex.org/W4294287515, https://openalex.org/W2077811884, https://openalex.org/W2015872152, https://openalex.org/W1979436597, https://openalex.org/W4386071758, https://openalex.org/W6760782946, https://openalex.org/W2991216808 |
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