Multi-Modal Biological Driver Monitoring via Ubiquitous Wearable Body Sensor Network

The objective of this paper is to introduce the design of the next generation driver monitoring platform to be facilitated in the semi-autonomous automotive system infrastructure. In the context of connected vehicles, this work extends current infrastructure to include real-time driver monitoring and feedback. Rather than leaving the driver out of the process, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of uncertainty ranges for driver biological state and behavior. The evolution and dissemination of mobile technology has created exceptional opportunities for highly detailed and personalized data collection in a far more granular and cost effective way. However, turning this potential into practice requires algorithms and methodologies to transform these raw data into actionable information. We have developed a robust driver monitoring platform consisting of automotive sensors (i.e. OBD-II) that capture the real-time information of the vehicle and driving behavior as well as a heterogeneous wearable body sensor network that collects the driver biometrics (e.g., electroencephalography (EEG) and electrocardiogram (ECG)). Accurate synchronization and storage of such multi-source heterogeneous data were also developed and validated. Finally, The task of characterizing driver distraction using EEG signals was investigated in two different road conditions as a proof of concept.