In this course, which is open to students with a basic knowledge of programming and digital logic, we will explore the fundamentals of AD systems—machine learning, computer vision, and hardware implementation on a field programmable gate array (FPGA). We will cover critical concepts such as object, vehicle, and lane detection, as well as traffic sign classification, AI, and deep learning algorithms. You will study practical systematic design of typical FPGA applications in AD systems using a hardware description language, such as VHDL or Verilog before moving to testbench development, simulation for bit-true design verification, and complete system design synthesis.

The course designed to strengthen theoretical understanding and provide hands-on experience with hardware. By the end of the course, you will have hands-on experience with FPGA design and be able to design, test, and implement a complete digital system on an FPGA device including interfacing to external devices.

Learning Objectives
At the conclusion of the course, you should be able to

• Define, develop, and model image processing and machine learning basic algorithms
• Understand and differentiate computation platforms for AD
• Develop basic RTL designs for FPGA
• Develop image processing algorithms on FPGA
• Implement, verify, and simulate a working design on FPGA for AD applications

Skills Needed:

Some programming knowledge (Python, MATLAB, VHDL, Verilog, SystemVerilog) and digital system design experience is preferred, but not required.