Systems and Algorithms for Autonomous Vehicles, 7.5 credits

Contents

The course provides an in-depth introduction to autonomous vehicles, addressing both the algorithms aspect and the systems aspect.
The course consists of two parts.

Part 1: Theory (5.5 hp). History and background of autonomous driving; perception-planning-control pipeline of autonomous vehicles; fundamentals of Machine Learning, Deep Learning and Reinforcement Learning; Convolutional Neural Networks for perception, including object classification, object detection, and segmentation; classic planning and control algorithms; Reinforcement Learning-based planning and control algorithms; safety and security issues; hardware and software platforms.

Part 2: Lab Assignments (2.0 hp). Training and deployment of Deep Learning models for autonomous driving with a framework such as Tensorflow or PyTorch; implementation of part of the perception-planning-control pipeline in a simulation environment.

Expected learning outcomes

After completing the course the student will be able to:

• describe the history and background of autonomous driving,
• describe the key concepts of the perception, planning and control pipeline for autonomous vehicles, 
• describe the key concepts of Machine Learning, Deep Learning and Reinforcement Learning, 
• describe the key concepts of Convolutional Neural Networks, 
• train and deploy Deep Learning models with a framework such as Tensorflow or PyTorch, 
• design and implement perception/control algorithms for autonomous driving in a simulation environment.

Required Knowledge

Admission to the course requires 120 credits of previous studies including courses in the field of Artificial Intelligence (AI) or Machine Learning of at least 7.5 credits

Form of instruction

The course is conducted in the form of lectures and laboratory exercises.

Examination modes

The examination is based on a written final exam and points assessed on laboratory exercises. One of the grades Fail (U), Pass (3), Pass with Credit (4) or Pass with Distinction (5) is given. To obtain grade (3), at least 50% of the maximum points are required. For grade (4), at least 65% of the maximum points, for grade (5), at least 80% of the maximum points.

Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaption of modes of assessment must give due consideration to the student's needs. The adaption of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaption of the examination, after which the student will be notified. 

Other regulations

In the event that the syllabus ceases to apply or undergoes major changes, students are guaranteed at least three examinations (including the regular examination opportunity) according to the regulations in the syllabus that the student was originally registered on for a period of a maximum of two years from the time that the previous syllabus ceased to apply or that the course ended.