Driving Scene Supplementary Class Balancing Network for Edge Driving Scene Recognition

Abstract

Driving scene data in the natural environments show an extremely imbalanced distribution, i.e. some scenes are in the majority while others are very rare. These rare driving scenes, also known as edge driving scenes, can dramatically affect the accuracy of driving scene recognition, as well as the safety, convenience, and intelligence of autonomous driving. Thus, this paper proposed the Driving Scene Supplementary Class Balancing Network to address edge driving scene recognition, which can effectively recognize edge driving scenes while training in imbalanced data. Our approach consists of three components. First, the Edge Scene Supplementer generates extremely rare scenes to supplement the training data to reduce the excessive imbalance between edge scenes and general scenes. Then, the Feature Encoder is responsible for efficiently extracting visual and temporal representations from the driving scenes. Finally, the Weight Balancing Classifier presents the classification results and re-balances the loss of driving scenes. To evaluate the performance of the proposed method, comprehensive experiments were performed on two datasets of real-world driving scenes. The results show that the proposed method outperforms the state-of-the-art methods in edge driving scene recognition.