Abstract:
This course integrates knowledge from across multiple areas of the electrical engineering discipline including electric machines and drive systems, control systems, estimation, signal processing, and optimization. It introduces the students to the basic principles of electrified autonomous vehicles through their involvement in a system integration project. The students will develop and integrate software and hardware modules for the McMaster Autonomous Electrified Vehicle (AEV), which is built on a small-scale (1/10th) RC vehicle platform. The goal is to develop sensing, planning, and control modules that allow the vehicle to operate in a range of scenarios from manual driving, through manual driving with driver assist, to fully autonomous driving. The first few weeks of the course will focus on the electric propulsion system of the vehicle, exploring topics in modelling and control of electric motor drives. The course will then move on to concentrate on autonomous driving aspects of the vehicle. In this part, the student will explore strategies for manual driving with collision avoidance assistance, as well as fully autonomous driving. Moreover, advanced topics of localization and mapping in autonomous systems will also be introduced. The students will be expected to gain practical skills in Linux OS, C/C++, Python, the Robot Operating System (ROS), Matlab/Simulink, and embedded systems in general. The knowledge integration objectives are achieved by organizing the project around weekly/biweekly deliverables and milestones, while providing some flexibility as to how these goals will be achieved.
