  In this study, we present a hierarchical fuzzy system by evaluating the risk
state for a Driver Assistance System in order to contribute in reducing the
road accident's number. A key component of this system is its ability to
continually detect and test the inside and outside risks in real time: The
outside car risks by detecting various road moving objects; this proposed
system stands on computer vision approaches. The inside risks by presenting an
automatic system for drowsy driving identification or detection by evaluating
EEG signals of the driver; this developed system is based on computer vision
techniques and biometrics factors (electroencephalogram EEG). This proposed
system is then composed of three main modules. The first module is responsible
for identifying the driver drowsiness state through his eye movements (physical
drowsiness). The second one is responsible for detecting and analysing his
physiological signals to also identify his drowsiness state (moral drowsiness).
The third module is responsible to evaluate the road driving risks by detecting
of the road different moving objects in a real time. The final decision will be
obtained by merging of the three detection systems through the use of fuzzy
decision rules. Finally, the proposed approach has been improved on ten samples
from a proposed dataset.
