Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment
This addresses a domain-specific challenge for autonomous driving in adverse weather, but appears incremental as it applies existing methods to new data without reporting concrete performance gains.
This paper tackles the problem of environment perception for autonomous cars in snowy conditions using semantic segmentation with Fully Convolutional Networks (FCN) trained on a custom dataset, but concludes that the database needs optimization and better algorithms are required for improved results.
This paper mainly focuses on environment perception in snowy situations which forms the backbone of the autonomous driving technology. For the purpose, semantic segmentation is employed to classify the objects while the vehicle is driven autonomously. We train the Fully Convolutional Networks (FCN) on our own dataset and present the experimental results. Finally, the outcomes are analyzed to give a conclusion. It can be concluded that the database still needs to be optimized and a favorable algorithm should be proposed to get better results.