Lecture: Self-Driving Cars

Within the last years, driverless cars have emerged as one of the major workhorses in the field of artificial intelligence. Given the large number of traffic fatalities, the limited mobility of elderly and handicapped people as well as the increasing problem of traffic jams and congestion, self-driving cars promise a solution to one of our socities most important problems: the future of mobility. However, making a car drive on its own in largely unconstrained environments requires a set of algorithmic skills that rival human cognition, thus rendering the task very hard. This course we will cover the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques. Topics include camera, lidar and radar-based perception, localization, navigation, path planning, vehicle modeling/control, imitation learning and reinfocement learning. The tutorials will deepen the acquired knowledge through the implementation of several deep learning based approaches to perception and sensori-motor control in the context of autonomous driving. Towards this goal, we will build upon existing simulation environments and established deep learning frameworks.

Students develop an understanding of the capabilities and limitations of state-of-the-art autonomous driving solutions. They gain a basic understanding of the entire system comprising perception, learning and vehicle control. In addition, they are able to implement and train simple models for sensori-motor control.

• Course number: ML-4340
• Credits: 9 ECTS
• Recommended for: Master, 3rd semester
• Total Workload: 270h
• This lecture is taught as flipped classroom. Lectures will be held asynchronously via YouTube (see sidebar for link). We will provide all lectures before the respective interactive live sessions for self-study. Please watch the relevant videos before participating in the interactive live sessions.
• Each week, we host an interactive live session where questions regarding the lecture and exercises are discussed together (see sidebar for details).
• We also offer a weekly zoom helpdesk where students may ask questions or share their screen to obtain individual feedback and support for solving the exercises (see sidebar for details).
• Exercises will not be graded. Instead, we will discuss the solution together.
• Students may obtain bonus points for the exam by participating in coding challenges and answering questions about the lectures and exercises in weekly quizzes which also serve as measure for self-motivation. All quizzes are provided via our Lecture Quiz Server (see sidebar for details).

• Basic Computer Science skills: Variables, functions, loops, classes, algorithms
• Basic Python and PyTorch coding skills
• Basic Math skills: Linear algebra, probability and information theory (eg., Math for ML lecture
  https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml/index.php)
  As a refresher we recommend reading Chapters 1-4 of: http://www.deeplearningbook.org
• Experience with Deep Learning (eg., through participation in our Deep Learning lecture)

• To participate in this lecture, you must enroll via ILIAS (see sidebar for link)
• Registration via ILIAS will open on 30.09. at 12:00
• Information about exam registration can be found here

The exercises play an essential role in understanding the content of the course. There will be 6 assignments in total. The assignments contain pen and paper questions as well as programming problems. Each programming problem involves programming an agent to participate in a challenge against the other agents developed by the students. The winners of each challenge will present their work during the final live session. For programming, the students will use Python and PyTorch, a deep learning framework which features GPU support and auto-differentiation. If you have questions regarding the exercises or the lecture, please ask them during the live sessions, at the zoom helpdesk or in our ILIAS forum.

• Student's Self-Driving Cars Lecture Notes
• Janai, Güney, Behl and Geiger: Computer Vision for Autonomous Vehicles
• Goodfellow, Bengio and Courville: Deep Learning
• Richard Szeliski: Computer Vision: Algorithms and Applications
• Deisenroth, Faisal and Ong: Mathematics for Machine Learning