Driving Behavior Signals and Machine Learning: A Personalized Driver Assistance System

The progressive integration of driver assistance systems (DAS) into vehicles in recent decades has contributed to improving the quality of the driving experience. Currently, there is a need for individualization of advanced DAS with the aim of improving safety, security and comfort of the driver. In particular, the need to adapt the vehicle to individual preferences and requirements of the driver is an important research focus. In this work, an individualized and non-intrusive monitoring system for real-time driver support is proposed. The kernel of the system is a driver identification module based on driving behavior signals and a high-performance machine learning technique. The scheme is suitable for the development of single-chip embedded systems. Moreover, most of the measurement units used in this research are nowadays available in commercial vehicles, so the deployment of the system can be performed with minimal additional cost. Experimental results using a reduced set of features are very encouraging. Identification rates greater than 75% are obtained for a working set of 11 drivers, 86% for five-driver groups, 88% for four-driver groups, and 90% for three-driver groups.