Eye Gazing Enabled Driving Behavior Monitoring and Prediction

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving behaviors only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving behaviors calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. We argue that a driver’s eyes are the interface, as this is the first and the essential window that gathers external information. The objective of the proposed research is to develop an active driving behavior monitoring and prediction framework for driving assistance applications, which is closely related to PANOMOTION TECHNOLOGY INC., a local startup working on intelligence driving assistants. The proposed research can greatly benefit the company by applying the research outcomes to its main products.

Faculty Supervisor:

Victor Leung

Student:

Xiaoyi Fan

Partner:

PanoMotion Technology Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program: