227-0560-00L Computer Vision and Artificial Intelligence for Autonomous Cars
| Semester | Autumn Semester 2024 |
| Lecturers | C. Sakaridis |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Up until FS2022 offered as Deep Learning for Autonomous Driving |
| Abstract | This course introduces the core computer vision techniques and algorithms that autonomous cars use to perceive the semantics and geometry of their driving environment, localize themselves in it, and predict its dynamic evolution. Emphasis is placed on techniques tailored for real-world settings, such as multi-modal fusion, domain-adaptive and outlier-aware architectures, and multi-agent methods. | |||||||||||||||||||||||||||
| Learning objective | Students will learn about the fundamentals of autonomous cars and of the computer vision models and methods these cars use to analyze their environment and navigate themselves in it. Students will be presented with state-of-the-art representations and algorithms for semantic, geometric and temporal visual reasoning in automated driving and will gain hands-on experience in developing computer vision algorithms and architectures for solving such tasks. After completing this course, students will be able to: 1. understand the operating principles of visual sensors in autonomous cars 2. differentiate between the core architectural paradigms and components of modern visual perception models and describe their logic and the role of their parameters 3. systematically categorize the main visual tasks related to automated driving and understand the primary representations and algorithms which are used for solving them 4. critically analyze and evaluate current research in the area of computer vision for autonomous cars 5. practically reproduce state-of-the-art computer vision methods in automated driving 6. independently develop new models for visual perception | |||||||||||||||||||||||||||
| Content | The content of the lectures consists in the following topics: 1. Fundamentals (a) Fundamentals of autonomous cars and their visual sensors (b) Fundamental computer vision architectures and algorithms for autonomous cars 2. Semantic perception (a) Semantic segmentation (b) Object detection (c) Instance segmentation and panoptic segmentation 3. Geometric perception and localization (a) Depth estimation (b) 3D reconstruction (c) Visual localization (d) Unimodal visual/lidar 3D object detection 4. Robust perception: multi-modal, multi-domain and multi-agent methods (a) Multi-modal 2D and 3D object detection (b) Visual grounding and verbo-visual fusion (c) Domain-adaptive and outlier-aware semantic perception (d) Vehicle-to-vehicle communication for perception 5. Temporal perception (a) Multiple object tracking (b) Motion prediction The practical projects involve implementing complex computer vision architectures and algorithms and applying them to real-world, multi-modal driving datasets. In particular, students will develop models and algorithms for: 1. Semantic segmentation and depth estimation 2. Sensor calibration for multi-modal 3D driving datasets 3. 3D object detection using lidars | |||||||||||||||||||||||||||
| Lecture notes | Lecture slides are provided in PDF format. | |||||||||||||||||||||||||||
| Prerequisites / Notice | Students are expected to have a solid basic knowledge of linear algebra, multivariate calculus, and probability theory, and a basic background in computer vision and machine learning. All practical projects will require solid background in programming and will be based on Python and libraries of it such as PyTorch. | |||||||||||||||||||||||||||
Competencies |
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