$\mathtt {Radar}$: Adversarial Driving Style Representation Learning With Data Augmentation

Characterizing human driver's driving behaviors from global positioning system (GPS) trajectories is an important yet challenging trajectory mining task. Previous works heavily rely on high-quality GPS data to learn such driving style representations through deep neural networks. However, they have overlooked the driving contexts that greatly govern drivers' driving activities and the data sparsity issue of practical GPS trajectories collected at a low-sampling rate. Besides, existing works omit the cold start problem, where the newly joined drivers usually have insufficient data to learn accurate driving style representations. To address these limitations, we present an adversarial driving style representation learning approach, named Radar. In addition to summarizing statistic features from raw GPS data, Radar also extracts contextual features from three aspects of road condition, geographic semantic, and traffic condition. We exploit the advanced semi-super vised generative adversarial networks to construct our learning model. By jointly considering statistic features and contextual features, the trained model is able to efficiently learn driving style representations from practical GPS trajectory data. Furthermore, we enhance Radar's representation learning for drivers owning limited training data with some basic data augmentation strategies and a novel auxiliary driver based data augmentation method. Experiments on two benchmark applications, i.e., driver identification and driver number estimation, with a large real-world GPS trajectory dataset demonstrate that Radar can outperform the state-of-the-art approaches by learning more effective and accurate driving style representations.