Identifying vehicle accidents and high risk drivers using Machine Learning

The primary objective of the project is to approach the problem of understanding true causality of vehicle accidents and scientifically determining which vehicles and drivers are at highest risk of an accident from a machine learning perspective. Geotab has a number of identified collisions in X, Y and Z planes, and much more. The research would be aimed at using both Geotabs data in addition to external data such as weather and topography to develop a predictive model that can identify those drivers at highest risk of an accident. This may be based solely on current driving behavior and/or the driving history.
The results of this project are important in helping our over 20,000 commercial fleet customers understand the true safety risks that exist in their fleet leveraging a novel machine learning approach that goes beyond a generic score. TO BE CONT’D

Faculty Supervisor:

Roger Grosse

Student:

Meng Zhang

Partner:

Geotab

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program: