Version 1
: Received: 11 July 2023 / Approved: 12 July 2023 / Online: 13 July 2023 (12:25:45 CEST)
How to cite:
Carvalho, M.; Hangan, H. Modelling Precipitation Intensity Impacting Vehicles in Motion. Preprints2023, 2023070859. https://doi.org/10.20944/preprints202307.0859.v1
Carvalho, M.; Hangan, H. Modelling Precipitation Intensity Impacting Vehicles in Motion. Preprints 2023, 2023070859. https://doi.org/10.20944/preprints202307.0859.v1
Carvalho, M.; Hangan, H. Modelling Precipitation Intensity Impacting Vehicles in Motion. Preprints2023, 2023070859. https://doi.org/10.20944/preprints202307.0859.v1
APA Style
Carvalho, M., & Hangan, H. (2023). Modelling Precipitation Intensity Impacting Vehicles in Motion. Preprints. https://doi.org/10.20944/preprints202307.0859.v1
Chicago/Turabian Style
Carvalho, M. and Horia Hangan. 2023 "Modelling Precipitation Intensity Impacting Vehicles in Motion" Preprints. https://doi.org/10.20944/preprints202307.0859.v1
Abstract
With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions on them. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or generate water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modelling covers a wide range of parameters, such as varying wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modelling results were partially validated against direct measurements carried out by a test vehicle. The model outputs showed strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. Dimensional Analysis of the problem has highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface on the perceived precipitation level. The proposed model is used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
