Towards a Model of Snow Accretion for Autonomous Vehicles
| Autor: | Mateus Carvalho, Sadegh Moradi, Farimah Hosseinnouri, Kiran Keshavan, Eric Villeneuve, Ismail Gultepe, John Komar, Martin Agelin-Chaab, Horia Hangan |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Atmosphere, Vol 15, Iss 5, p 548 (2024) |
| Druh dokumentu: | article |
| ISSN: | 15050548 2073-4433 |
| DOI: | 10.3390/atmos15050548 |
| Popis: | Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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