Numerical Investigation of Snow Accumulation on a Sensor Surface of Autonomous Vehicle 2020-01-0953
Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARS and RADARs. This information is processed by the on board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge. In general, wind tunnel experiments or field tests are expensive and time-consuming for evaluation of snow accumulation on AV sensor surfaces. Instead, prediction of snow accumulation by numerical approach is faster and more cost-effective. Computational Fluid Dynamics (CFD) with Lagrangian Particle Method (LPM) and Discrete Element Method (DEM) are suitable for predicting snow particle behavior. In this study, both LPM and DEM approaches were used to predict the deflection of snow particles away from a camera lens. In addition, in order to evaluate if the snow particles can be removed after they hit and stick on the camera lens by aerodynamic force, a new metric called Snow Removing Potential (SRP) was developed. Final CFD simulation results from a combination of LPM method and SRP function were correlated well with the observed snow accumulation patterns on the camera lens from the snow tunnel tests.
Haiping Hong, Mohammed Rizwan Bathusha, Sunil Patil, Behrouz Mohammadian, Venkatesh Krishnan, Mark Fountain, Hossein Sojoudi
Ford Motor Company, University of Toledo