227-0560-00L Deep Learning for Autonomous Driving
| Semester | Spring Semester 2020 |
| Lecturers | D. Dai, A. Liniger |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Registration in this class requires the permission of the instructors. Class size will be limited to 80 students. Preference is given to EEIT, INF and RSC students. |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 227-0560-00 V | Deep Learning for Autonomous Driving
 Permission from lecturers required for all students.
| 3 hrs |
| D. Dai, A. Liniger | ||||||
| 227-0560-00 P | Deep Learning for Autonomous Driving
Permission from lecturers required for all students.
| 2 hrs |
| D. Dai, A. Liniger |
Catalogue data
| Abstract | 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. |
| Objective | 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: 1) understand the core technologies of building a self-driving car; 2) have a good overview over the current state of the art in self-driving cars; 3) be able to critically analyze and evaluate current research in this area; 4) be able to implement basic systems for multiple autonomous driving tasks. |
| Content | 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: I) Foundation a) Fundamentals of a self-driving car b) Fundamentals of deep-learning II) Perception a) Semantic segmentation and lane detection b) Depth estimation with images and sparse LiDAR data c) 3D object detection with images and LiDAR data d) Object tracking and motion prediction III) Localization a) GPS-based and Vision-based Localization b) Visual Odometry and Lidar Odometry IV) Path Planning and Control a) Path planning for autonomous driving b) Motion planning and vehicle control c) Imitation learning and reinforcement learning for self driving cars 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: - Sensor calibration and synchronization to obtain multimodal driving data; - Semantic segmentation and depth estimation with deep neural networks ; - Learning to drive with images and map data directly (a.k.a. end-to-end driving) |
| Lecture notes | The lecture slides will be provided as a PDF. |
| Prerequisites / Notice | 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. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | D. Dai, A. Liniger |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| Mode of examination | oral 30 minutes |
| Additional information on mode of examination | The grade is based on (1) the realization of three projects (10%, 20% and 20%), and (2) an oral session exam (50%). Successfully completing the projects is compulsory for attending the exam. The projects will be group based. The examination is based on the contents of the lectures, the associated reading materials and exercises. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | Course Website |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| General |  : Special students and auditors need a special permission from the lecturersPermission from lecturers required for all students |
