Robust Hierarchical Deep Learning for Vehicular Management

Congestion detection is an important aspect of vehicular management. However, most of the existing algorithms are insufficient for real applications. Traditional features are not discriminative which results in rather poor performance under complex scenarios. The deep features can better represent high-level information, but the training of deep network for regression is difficult. To promote the congestion detection, a robust hierarchical deep learning is proposed for the task. In this method, a deep network is designed for hierarchical semantic feature extraction. Different from traditional deep regression networks, which usually directly utilize mean squared error as loss function, a robust metric learning is employed to effectively train the network. Based on this, multiple networks are combined together to further improve the generalization ability. Extensive experiments are conducted and the proposed model is confirmed to be effective.