AI for Autonomous Systems

Autonomous systems focus on autonomy and mobility platforms that include robots, self-driving cars, and unmanned aerial vehicles (UAVs), to name a few.  This technical track covers a wide range of topics on machine learning, AI, computer vision, embedded and sensing systems, control systems, and robotics. Although the societal benefits of autonomous systems vary depending upon the specific application, each of the established and emerging applications of AI for autonomous systems is intended to improve the quality, safety, and/or security of human life. For example, self-driving cars are expected to reduce emissions, provide mobility to the elderly and disabled, reduce transportation costs, increase safety, and reduce crime. Humanoid robots can provide personal assistance to the disabled, sick, and elderly, perform dirty or dangerous jobs that pose health or safety risks to humans, or perform tasks that are beyond human capability. Common to each of the applications is the need to attain, analyze, and act upon multifaceted information and data obtained from a variety of sensors such as radar, lidar, CCD cameras, MEMS devices, etc.

Potential employers for students in this technical track include Google, Microsoft, Waymo, Apple, Amazon, established and emerging automotive and robot manufacturers, and aerospace companies.

Along with faculty advisors, each student will design his or her Plan of Study. Students can acquire technical depth in AI concepts and algorithms including supervised and unsupervised machine learning, neural networks, computer vision, pattern recognition, sensor fusion, linguistics, and decision making. They can acquire “breadth at the edges” by taking courses on hybrid electric vehicles, embedded systems, analysis of data and design of experiments. Students with a background in computer engineering may find opportunities in the design of embedded and distributed sensor-infused control systems. Relevant Courses

Each student will consult with faculty advisors and develop a Plan of Study tailored for their goals and background.  Some relevant courses for this technical topic are listed below.

Technical Concentration (12 credits)

ECE 570 Artificial Intelligence
ECE 595-1 Machine Learning I
ECE 595-2 Machine Learning 2
ECE 629 Neural Networks
ECE 661 Computer Vision
ECE 569 Introduction to Robotic Systems
ECE 59500 Primer on Analysis of Experimental Data and Design of Experiments

Technical Breadth (6 credits)

ECE 51018 Hybrid vehicles
ECE 55900 VLSI Systems
ECE 59500 Introduction to Embedded systems
ECE 59500 Primer on Analysis of Experimental Data and Design of Experiments
ECE 60400 Electromagnetic Fields 

Mathematics (3 credits)

MA 511 (Linear Algebra)
MA 514 Numerical Analysis

Ideas to Innovation Project and Skills Development (9 credits)

Several of the project ideas listed are relevant to this technical focus.

30 credits total