Snow coverage estimation using camera data for automated driving applications

Abstract

In the U.S., over 38,000 fatalities occur every year due to automotive accidents where 24% of these accidents are attributable to inclement weather. Automated driving systems have shown to decrease up to 21% of potential collisions, however, these systems do not operate in inclement weather. The camera’s reliance on clear lane line detections cease the functionality of the safety systems when occlusions occur due to precipitation. For these systems to become operational during conditions such as snow coverage, therefore leading to a greater impact on safety, new research and development is needed to focus on inclement weather scenarios. This study addresses this need by first collecting a new dataset consisting of raw camera images along arterial roads in Kalamazoo, MI and additionally collecting snow precipitation data from the National Center for Environmental Information. With this data, snow coverage estimation models were developed to automatically determine categories of snow coverage. The models were developed by investigating various machine learning algorithm types, image predictors, and the presence of snow precipitation data. The final model resulted in 95.63% accuracy for categorizing the instance as either none, standard, or heavy snow coverage. These categories are important for future development of purpose-build algorithms that identify drivable regions in various levels of snow coverage for future automated driving systems. The results demonstrate that snow estimation is a near-term achievable task and that the presence weather data improves accuracy. With the addition of snow-coverage estimation, automated driving systems can be developed to react to these different conditions respectively and further reduce the nearly 6,000 annual fatalities caused driving in adverse weather.