DriverRep: Driver identification through driving behavior embeddings.

Driver identification has emerged as an active field of study to further personalize the integrated advanced driver-assistance systems into intelligent vehicles, provide security and safety for ride-hailing services, and prevent auto theft. Several studies, within recent years, have investigated non-intrusive identification approaches, focusing on driving behavior analysis to characterize drivers' driving behavior, in which a considerable volume of labeled data is required. This paper develops a deep learning architecture, namely DriverRep, to extract the latent representations associated with each individual, called driver embeddings. These embeddings represent the unique driving characteristics of drivers. To this aim, we introduce a fully unsupervised triplet loss that selects triplet samples from data in an unsupervised manner and extracts the embeddings using our proposed stacked encoder architecture. Dilated causal convolutions are used to make residual blocks of the encoder. To perform the task of driver identification, we leverage the classification accuracy of SVM on top of the obtained driver embeddings. The evaluation results over two datasets, each of which contains ten drivers, reveal that the DriverRep can successfully capture the underlying features within the data and outperform benchmark driver identification schemes, obtaining an average accuracy of 94.7% and 96% for two-way and three-way identification, respectively. We also investigate the ability of the DriverRep in handling sparsely labeled data. The results represent substantial improvements in comparison with the supervised approaches when applied to highly sparsely labeled data. • A novel driver identification method is proposed based on driver embeddings. • A novel strategy is introduced for unsupervised latent representation learning. • A novel encoder architecture based on stacked of dilated causal convolutions is designed to extract driver embeddings. • The proposed method outperforms existing driver identification schemes. • The proposed method outperforms time series classification models applied on highly sparsely labeled data. [ABSTRACT FROM AUTHOR]