Automotive Modular Real-time Edge Computing on Embedded Hardware

As vehicle systems become more advanced, accurate information about vehicle states, parameters, and surrounding operating conditions becomes paramount for vehicle health monitoring and driver control systems. This information is vital for the production, cost, and efficiency of the vehicles, as well as a key to improving passenger safety. In commercial vehicles, not all parameters of interest are directly measurable using sensors, because of sensor costs or design constraints. This research aims to bring developed estimation algorithms to embedded hardware for application to commercial vehicles.

Faculty Supervisor:

Amir Khajepour

Student:

Partner:

Audesse

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program: