Winter Adverse Driving dataSet (WADS): Year Three
Document Type
Conference Proceeding
Publication Date
6-6-2022
Department
Department of Electrical and Computer Engineering
Abstract
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.
Publication Title
Proceedings of SPIE - The International Society for Optical Engineering
ISBN
9781510651067
Recommended Citation
Kurup, A.,
&
Bos, J.
(2022).
Winter Adverse Driving dataSet (WADS): Year Three.
Proceedings of SPIE - The International Society for Optical Engineering,
12115.
http://doi.org/10.1117/12.2619424
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16244