Perceived precipitation intensity prediction model based on simultaneous dynamic and static observations for evaluating weather impacts on vehicle applications

Abstract

Adverse weather conditions increase road risks; thus, weather testing is necessary to evaluate vehicle performance. Outdoor testing is the most realistic, but it is not as controlled, repeatable, and rapid as indoor testing using an artificial precipitation system. However, outdoor data is still desirable for establishing the simulation targets on the vehicle surfaces. The dynamic-to-static precipitation intensity ratio is a useful parameter to correlate natural precipitation with perceived precipitation experienced by the moving vehicle. Theoretically, the amount of precipitation experienced by a translating surface depends on the orientation and travel speed. However, there are other external factors that could affect the perceived intensity, such as wind, turbulence, and droplet size distribution (DSD). Therefore, the existing simplified models evaluating a number of droplet strikes or precipitation flux calculated using natural precipitation density fail to have accurate predictions of the perceived precipitation rate, which hinders the evaluation vehicle application performance, such as sensor perception. In the present work, a semi-empirical prediction model is developed from the physics of precipitation in the context of vehicle aerodynamics and atmospheric dynamics. This model is validated with outdoor testing on a track for three days with rainy conditions. Multiple optical disdrometers are used to evaluate the precipitation rate experienced by a moving vehicle at different surface orientations through meteorological observations obtained in real-time from a nearby stationary meteorological tower and a moving vehicle. The data acquisition and processing methods are presented in detail. Results suggested that the proposed model is found to improve the current simplified mathematical expressions and is repeatable. It is found that improvements in prediction accuracy of perceived precipitation intensity compared to existing methods are usually more than 50%.