Feature selection for Deep Learning applied to the identification of impaired drivers

DriveABLE Inc uses a set of simple video tasks to identify the impaired drivers. Video tasks come in the form of simple games and measure cognitive ability. The test results are analysed by AI powered algorithm that predicts the impairment level of the driver. Our projects main objective is to redesign the AI in such a way that it can cover more use cases with fewer tasks. In particular we will redesign the algorithm so that it will accept incomplete tests. We will also identify redundant games in order to make overall test shorter. In addition this analysis will allow to highlight important characteristic of tasks which will lead to new generation of improved tasks.

Faculty Supervisor:

Linglong Kong

Student:

Borislav Mavrin

Partner:

DriveABLE Inc

Discipline:

Mathematics

Sector:

Medical devices

University:

Program: