Learning objectives
The course aims to examine various image processing algorithm focused on the perception for a "smart" vehicle.
The student will learn problems related to image processing for vehicles and in the laboratory will implement algorithms that are normally used for security and to allow the autonomous navigation of a vehicle .
Prerequisites
A basic knowledge of image processing, C ++ programming, linear algebra and numerical calculation is required.
Course unit content
Inside the course will be presented and implement some agoritmi of image processing for vehicles: lane detection, detection, classification and tracking of obstacles and visual odometry. These algorithms are part of the group of "Advanced driver assistance systems" that will allow in the future to develop fully autonomous intelligent vehicles.
Full programme
- Introduction to the framework of image processing
- Vehicle Issues
- Vehicle Sensors
- Lane Detection
- Classification and Deep Learning
- Identifying obstacles
- Tracking
- Sensor Fusion
- Visual Odometry
Bibliography
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Teaching methods
The main topics of the course will benefit from laboratory activities and demonstations.
Assessment methods and criteria
Written test. Practial Test.
Other information
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2030 agenda goals for sustainable development
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