Winter adverse driving dataset (WADS): year three

Akhil Kurup; Jeremy Bos

doi:10.1117/12.2619424

6 June 2022 Winter adverse driving dataset (WADS): year three

Akhil Kurup, Jeremy Bos

Proceedings Volume 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022; 121150H (2022) https://doi.org/10.1117/12.2619424
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Akhil Kurup and Jeremy Bos "Winter adverse driving dataset (WADS): year three", Proc. SPIE 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, 121150H (6 June 2022); https://doi.org/10.1117/12.2619424

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