Multitask Learning Assisted Driver Identity Authentication and Driving Behavior Evaluation
Yijie Xun, Jiajia Liu, Zhenjiang Shi
Abstract
The industrial Internet of Things has become the new driving force for the automobile industry, making people's travel increasingly convenient. However, there are still a multitude of challenges that need to be tackled, including but not limited to illegal driver detection, legal driver identification, and driving behavior evaluation. At present, many researchers have attempted to solve issues of illegal driver detection and legal driver identification by using deep learning network, but there are still quite a few limitations in the collection and analysis of driving behavior data. Moreover, the problem of driving behavior evaluation has been paid little attention. Therefore, in this article we conduct a comprehensive study on driving behavior habits and establish a multitask learning (MTL) network to solve the abovementioned problems. First, we collect original data from a real vehicle and extract the driving behavior characteristics. Then, a novel MTL network composed of long short-term memory network, support vector domain description model and feedforward neural network is established, which achieves illegal driver detection, legal driver identification, and driving behavior evaluation. Extensive experiments illustrate that the proposed MTL network not only supports parallel learning to reduce time and space costs, but also has excellent performances and robustness for the three tasks.