Winter Adverse Driving dataSet (WADS): Year Three
Michigan Tech's unique climatology allows for relatively effortless collection of autonomous vehicle winter driving data featuring notionally severe winter weather. Over the past two years we have collected over twenty-five terabytes of winter driving data in suburban and rural settings. Year o...
Published in: | Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022 |
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ftmichigantuniv:oai:digitalcommons.mtu.edu:michigantech-p-35544 2023-05-15T15:03:10+02:00 Winter Adverse Driving dataSet (WADS): Year Three Kurup, Akhil Bos, Jeremy 2022-06-06T07:00:00Z https://digitalcommons.mtu.edu/michigantech-p/16244 https://doi.org/10.1117/12.2619424 unknown Digital Commons @ Michigan Tech https://digitalcommons.mtu.edu/michigantech-p/16244 https://doi.org/10.1117/12.2619424 Michigan Tech Publications Autonomous perception Autonomous Vehicles datset LiDAR RADAR Department of Electrical and Computer Engineering Electrical and Computer Engineering text 2022 ftmichigantuniv https://doi.org/10.1117/12.2619424 2022-09-15T17:44:56Z Michigan Tech's unique climatology allows for relatively effortless collection of autonomous vehicle winter driving data featuring notionally severe winter weather. Over the past two years we have collected over twenty-five terabytes of winter driving data in suburban and rural settings. Year one focused on phenomenology of snowfall in the context of autonomous vehicle sensors, specifically LiDAR. Year two focused on more severe conditions, longer wavelength LiDAR, and first attempts at applying perception pipeline processing to the dataset. For year three we focus on simultaneous RADAR and LiDAR data collection in arctic-like conditions and LiDAR designs likely to be used in ADAS and production autonomous vehicles. We also introduce a point-wise labeled portion of our dataset to aid machine learning based autonomy and a snow removal filter to reduce clutter noise and improve existing object detection algorithms. Text Arctic Michigan Technological University: Digital Commons @ Michigan Tech Arctic Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022 16 |
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Michigan Technological University: Digital Commons @ Michigan Tech |
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Autonomous perception Autonomous Vehicles datset LiDAR RADAR Department of Electrical and Computer Engineering Electrical and Computer Engineering |
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Autonomous perception Autonomous Vehicles datset LiDAR RADAR Department of Electrical and Computer Engineering Electrical and Computer Engineering Kurup, Akhil Bos, Jeremy Winter Adverse Driving dataSet (WADS): Year Three |
topic_facet |
Autonomous perception Autonomous Vehicles datset LiDAR RADAR Department of Electrical and Computer Engineering Electrical and Computer Engineering |
description |
Michigan Tech's unique climatology allows for relatively effortless collection of autonomous vehicle winter driving data featuring notionally severe winter weather. Over the past two years we have collected over twenty-five terabytes of winter driving data in suburban and rural settings. Year one focused on phenomenology of snowfall in the context of autonomous vehicle sensors, specifically LiDAR. Year two focused on more severe conditions, longer wavelength LiDAR, and first attempts at applying perception pipeline processing to the dataset. For year three we focus on simultaneous RADAR and LiDAR data collection in arctic-like conditions and LiDAR designs likely to be used in ADAS and production autonomous vehicles. We also introduce a point-wise labeled portion of our dataset to aid machine learning based autonomy and a snow removal filter to reduce clutter noise and improve existing object detection algorithms. |
format |
Text |
author |
Kurup, Akhil Bos, Jeremy |
author_facet |
Kurup, Akhil Bos, Jeremy |
author_sort |
Kurup, Akhil |
title |
Winter Adverse Driving dataSet (WADS): Year Three |
title_short |
Winter Adverse Driving dataSet (WADS): Year Three |
title_full |
Winter Adverse Driving dataSet (WADS): Year Three |
title_fullStr |
Winter Adverse Driving dataSet (WADS): Year Three |
title_full_unstemmed |
Winter Adverse Driving dataSet (WADS): Year Three |
title_sort |
winter adverse driving dataset (wads): year three |
publisher |
Digital Commons @ Michigan Tech |
publishDate |
2022 |
url |
https://digitalcommons.mtu.edu/michigantech-p/16244 https://doi.org/10.1117/12.2619424 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Michigan Tech Publications |
op_relation |
https://digitalcommons.mtu.edu/michigantech-p/16244 https://doi.org/10.1117/12.2619424 |
op_doi |
https://doi.org/10.1117/12.2619424 |
container_title |
Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022 |
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1766335062651961344 |