Ìý
Graduate Catalog 2023-2024
Smart and Autonomous Vehicle (Certificate)
Description
The Graduate Certificate in Smart and Autonomous Vehicles (SAV) provides 91³Ô¹Ï×ÄÐÈ˵ijԹÏÍø and practicing engineers the technical skills and systems knowledge needed to be effective contributors to the development of self-driving vehicles and Advanced Driver-Assistance Systems (ADAS). The certificate exposes 91³Ô¹Ï×ÄÐÈ˵ijԹÏÍø to the broad component areas of motion capability, sensing and perception (with a focus on computer vision), mapping and localization, and algorithms for control and cognition.
Program Learning Outcomes
Students completing the SAV certificate program will have the ability to:
- Explain the basic levels of driving automation and operation of common ADAS technologies, as well as the operation of common intelligent transportation system architectures.
- Explain the principles of operation of the various sensors and actuators common to autonomous and semi-autonomous vehicles.
- Analyze the motion of a ground vehicle based on its mechanical architecture.
- Interpret sensory input data through perception algorithms, including image processing algorithms (for such tasks as lane and obstacle recognition).
- Map an unknown environment.
- Estimate aspects of a vehicle’s motion (position, velocity, etc.) based on sensor data.
- Design algorithms for the real-time control of an autonomous vehicle and its subsystems.
- Apply and develop high-level cognitive algorithms for perception, and the navigation and planning of an autonomous vehicle.
- Implement vehicle, subsystem, and communication algorithms in software and/or hardware.
- Employ simulation tools to implement and validate algorithms pertaining to vehicle and subsystem operation.
Admission Requirements
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)
Program Contact Information
Paul Spadafora
Director of Professional Engineering Programs & Industry Liaison
Office: Engineering 208
Telephone: 313-993-1603
Email: spadafpa@udmercy.edu
Valarie Steppes-Glisson
Administrative Assistant, Professional Engineering Programs
Office: Engineering 202
Telephone: 313-993-1128
Fax: 313-993-1955
Email: glissovs@udmercy.edu