A Brain-operated Advanced Driver-Assistance System for Collision Avoidance
According to the World Health Organization (WHO), 1.25 million deaths result from road accidents every year. Consequently, lots of technologies have been developed in order to reduce the accidents to save people’s lives. This project aims at developing an advanced driver-assistance system to increase the safety of the car by preventing collisions in emergency situations. It combines inputs from the surrounding environment with driver’s brain Electroencephalography (EEG) signals. By analyzing EEG activity, emergency braking can be initiated before the driver executes the physical action of braking. This could help in taking an appropriate action, whether by performing a brake and/or steer maneuvers, to prevent serious accidents. Temporal signatures in the recorded EEG that correspond to emergency braking will be extracted from the recorded EEG. Such signatures will be used to train different machine learning algorithms that will be subsequently used to decode the driver’s EEG activity to infer his/her intention. Implemented algorithms will be examined on data that will be recorded during the project using the Emotiv EPOC neuroheadset. The proposed system will also comprise a computer vision component that will analyze the surrounding driving environment and augment the EEG-based decision. The proposed system will be then tailored to operate within the AUTOSAR framework.