Automated Driver Drowsiness Control Technology Using Artificial Intelligence-based Decision Support System

The main purpose of this project is to develop the methodology to detect and predict driver drowsiness at the early stages using physical and physiological variables. A feasibility test is conducted to evaluate the accuracy and performance of the proposed methodology. The existing databases are leveraged to extract the required data. Signal processing, image processing, AI techniques and decision-making methods are utilized to analyze data for monitoring, detecting, predicting and controlling driver drowsiness. Finally, the ethics application is prepared and submitted to be applied for data collection in the future research experiments.
The other goal of this project is to perform the market research by collaborating with the L2M team. Approximately, 100 potential customers are interviewed to explore the customer pains and the probable solutions for addressing the existing problem concerning driver drowsiness. The collected interview data will then be analyzed to validate the defined hypothesis that leads to making decision whether the process of product development and deployment continued or not.

Faculty Supervisor:

Birsen Donmez

Student:

Vahid Abolhasannejad

Partner:

Springboard Atlantic

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

University of Toronto