A Hierarchical Fuzzy System for an Advanced Driving Assistance System

TL;DR

This paper introduces a hierarchical fuzzy system for advanced driver assistance that integrates real-time detection of road risks and driver drowsiness using computer vision and EEG analysis to enhance road safety.

Contribution

It presents a novel hierarchical fuzzy system combining vision and biometric data for real-time risk assessment in driver assistance systems.

Findings

System successfully detects driver drowsiness and road risks in real time

Improved safety by merging multiple risk detection modules

Validated on a dataset of ten samples

Abstract

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.