DriverRep: Driver identification through driving behavior embeddings

Mozhgan Nasr Azadani, Azzedine Boukerche

Abstract: Highlights•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.