Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving Data

Danger-level analysis is widely used to prevent potential driving risks based on driving performance. Such analysis is essential for monitoring driver performance. Moreover, danger-level analysis is vital for automotive safety systems and driving assistance applications. However, danger-level analysis that simultaneously considers driver-, vehicle-, and road-related information from driving data has rarely been conducted. Such analysis is very challenging due to the issues associated with the high volume and high variety in multisourced driving data. In this paper, we propose a novel danger-level analysis framework for dealing with high variety and high volume problems of multisourced driving data. Built upon a feature extraction method in the proposed framework, we first profile multisourced driving features for overcoming the variety problem. Next, danger-level analysis is formulated as a multiobjective pursuit problem in a linear model. The problem is then solved using a semisupervised learning strategy to overcome the volume issue. Therefore, the danger level can be accurately estimated from multisourced driving data by using the proposed framework. The experimental results indicate that the proposed framework outperforms existing machine learning techniques for multisourced driving data.