LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments

Author:

Park Ji-il1ORCID,Jo Seunghyeon2ORCID,Seo Hyung-Tae3ORCID,Park Jihyuk4ORCID

Affiliation:

1. National Defense AI Center, Agency for Defense Development (ADD), 160, Bugyuseong-daero 488beon-gil, Yuseong-gu, Daejeon 34060, Republic of Korea

2. AUTOCRYPT GmbH, Salvatorplatz, 80333 München, Germany

3. Department of Mechanical Engineering, College of Creative Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Suwon 16227, Republic of Korea

4. Department of Automotive Engineering, College of Digital Convergence, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea

Abstract

Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.

Funder

Ministry of Oceans and Fisheries

Yeungnam University

Publisher

MDPI AG

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