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  Program Learning Outcomes

  Students completing the SAV certificate program will have the ability to:

  1. Explain the basic levels of driving automation and operation of common ADAS technologies, as well as the operation of common intelligent transportation system architectures.
  2. Explain the principles of operation of the various sensors and actuators common to autonomous and semi-autonomous vehicles.
  3. Analyze the motion of a ground vehicle based on its mechanical architecture.
  4. Interpret sensory input data through perception algorithms, including image processing algorithms (for such tasks as lane and obstacle recognition).
  5. Map an unknown environment.
  6. Estimate aspects of a vehicle’s motion (position, velocity, etc.) based on sensor data.
  7. Design algorithms for the real-time control of an autonomous vehicle and its subsystems.
  8. Apply and develop high-level cognitive algorithms for perception, and the navigation and planning of an autonomous vehicle.
  9. Implement vehicle, subsystem, and communication algorithms in software and/or hardware.
  10. Employ simulation tools to implement and validate algorithms pertaining to vehicle and subsystem operation.
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  Admission Requirements

  Applicants will be considered if they have excellent academic records, having earned a minimum GPA of 3.0 in an undergraduate degree in engineering, or a closely related field with sufficient background. All incoming students will require experience with algorithmic thinking and programming in an established language (MATLAB, C++, Java, Python, etc.). Applicants will also need a strong core in mathematics including calculus, differential equations, matrix algebra, and probability. Students should further have experience with the analysis and design of feedback systems, including in the frequency domain. Each applicant must also be approved by the program director/chair and the College of Engineering & Science dean's office.
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  Graduate Certificate in Smart and Autonomous Vehicles Requirements (15 credits)

  This is a 15-17-credit (five courses, plus labs if necessary) certificate program. Nine credits (three courses) are required core courses, and 6-8 credits (two courses, plus labs if necessary) are electives, which may be chosen from the list below.  Students must maintain a 3.0 GPA in both the program and overall at the graduate level.

  Required Courses (9 credit hours)

  • ELEE 5350 Machine Learning (3 credits)
  • ELEE 5750 Deep Learning (3 credits)
  • ELEE 5760 Digital Control Theory (3 credits)

  Elective courses (choose 2 courses, plus labs if necessary, 6-8 credit hours)

  • ELEE 5200 Autonomous Mobility Robotics (3 credits)
  • ELEE 5400 Computational Intelligence
  • ELEE 5620 Random Variables and Random Processes (3 credits)
  • ELEE 5685 Wireless Sensor Networks (3 credits)
  • ELEE 5695 Wireless Sensor Networks Laboratory (1 credit)
  • ELEE 5700 Controls II (3 credits)
  • ELEE 5770 Embedded Systems (3 credits)
  • ELEE 5790 Embedded Systems Laboratory (1 credits)
  • ELEE 5810 Applications of Estimation Theory in Robotics & Signal Processing (3 credits)
  • ELEE 5920 Image Processing and Computer Vision (3 credits)    
  • ELEE 5940 Special Topics in Electrical & Computer Engineering  (if approved by program director) (3 credits)
  • ENGR 5220 Sensors and Actuators (3 credits)
  • MENG 5760 Vehicle Dynamics (3 credits)
  • CSSE 5480 Artificial Intelligence (3 credits)