Autonomous driving has moved from the realm of science fiction to a very real possibility during the past twenty years, largely due to rapid developments of deep learning approaches, automotive sensors, and microprocessor capacity. This course covers the core techniques required for building a self-driving car, especially the practical use of deep learning through this theme.
Students will learn about the fundamental aspects of a self-driving car.
They will also learn to use modern automotive sensors and HD navigational maps, and to implement, train and debug their own deep neural networks
in order to gain a deep understanding of cutting-edge research in autonomous driving tasks, including perception, localization and control.
After attending this course, students will:
We will focus on teaching the following topics centered on autonomous driving:
deep learning, automotive sensors, multimodal driving datasets, road scene perception, ego-vehicle localization, path planning, and control.
The course covers the following main areas:
The exercise projects will involve training complex neural networks and applying them on real-world, multimodal driving datasets. In particular, students should be able to develop systems that deal with the following problems:
This is an advanced grad-level course. Students must have taken courses on machine learning and computer vision or have acquired equivalent knowledge. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. All practical exercises will require basic knowledge of Python and will use libraries such as PyTorch, scikit-learn and scikit-image.
Registration for this class requires the permission of the instructors. Preference is given to EEIT, INF and RSC students.