Snow coverage estimation using camera data for automated driving applications

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, auto-mated 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.