OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification

Driver classification is used recently for vehicle anti-burglary and fake driver accounts based on driving behavior. Anti-burglary is a challenging problem as it leans on external devices to defend against vehicle theft. Several researchers analyzed the driving behavior to identify drivers, but they faced several challenges to produce a stable model for the cold start problem and for medium-long sequences. In addition, some approaches had an unpleasant performance when the action space increased (> 2 drivers). In this paper, we propose a novel approach named OnlineDC (Online Driver Classification), which leverages temporal driving behavior to identify a human subject behind the wheel. Our method utilizes the Gated Recurrent Unit (GRU) and the ResNet with the Squeeze-Excite blocks (SE) to analyze the long-short term patterns of driving behaviors. Moreover, we fostered the performance by building and applying the Feature Generation (FG) algorithm to extract spectral, temporal, and statistical features from the sensing data of vehicles. We conducted extensive experiments to show how our approach outperformed state-of-the-art baseline methods. The results also showed that our solution could resolve the cold-start problem for short patterns.