Learning objectives
The course aims to examine various perception-focused image processing algorithms for an 'intelligent' vehicle. Students will learn about problems related to image processing for vehicles and, in the laboratory, they will implement algorithms that are typically used for safety and to enable autonomous navigation of a vehicle.
Prerequisites
A basic knowledge of image processing, C ++ programming, linear algebra and numerical calculation is required.
Course unit content
This course will cover a range of image processing algorithms essential for intelligent vehicles. Students will be introduced to and implement algorithms such as lane detection, obstacle detection, classification and tracking of obstacles, and visual odometry. Additionally, the course will delve into the theory and application of the Data Fusion algorithm, and discuss various vehicle sensors and their technologies. These algorithms and technologies are integral components of 'Advanced Driver Assistance Systems' (ADAS) and serve as foundational elements for the development of fully autonomous intelligent vehicles.
Full programme
- Introduction
- Vehicle Issues
- Vehicle Sensors
- Data Fusion
- Sensor Calibration
- Visual Odometry
- Lane Detection
- Identifying obstacles
- FreeSpace, Occupacy Grid and Stixels
- Visual Self Localization and Visual SLAM
Bibliography
- - -
Teaching methods
The main topics of the course will benefit from laboratory activities and demonstations.
Assessment methods and criteria
Evaluation of laboratory activity, a written test and development of a research project.
Other information
- - -
2030 agenda goals for sustainable development
- - -
