TSFS12 Autonomous vehicles – planning, control, and learning systems, 6 hp for D, I, M, and Y

Lecturers: Erik Frisk (erik.frisk@liu.se) and Jan Åslund (jan.aslund@liu.se).

Please contact us for further information.

Note: This is a static page, for links to reading material, lecture notes, and other course material, see the public git repository at https://gitlab.liu.se/vehsys/tsfs12.

Autonomous Vehicles

Currently, an enormous interest in autonomous vehicles both within academia and industry, with applications for example in self-driving cars and trucks, and unmanned aerial vehicles. This course provides the theoretical and technological basis for how such systems work, and in particular covers algorithms and tools for planning, control, and learning in autonomous vehicles. An autonomous system often consists of components for planning, control, and learning and the interaction between these is studied from a system perspective with a strong implementation focus.

Below is an illustration of a typical architecture for an autonomous vehicle, adapted from Paden et al., IEEE Trans. on intelligent vehicles (2016). Autonomous system architecture

The objective of the course is to cover all parts of such an architecture and give a theoretical, technological, and practical foundation for how planning and control for autonomous vehicles can be realized in complex scenarios. The overall aim is an understanding of how methods from different fields can be integrated and applied in autonomous vehicles.

After passing the course, a student should be able to:

Course organization

The course is organized into lectures, exercises, hand-in assignments and a final mini-project. There is no final, written, exam in the course and the examination is based on the hand-in assignments and mini projects.

To pass the course with a grade of 3, it is required to complete five hand-in assignments and present them in either written or oral format (examination form varies), and complete a final project. The final project typically involves experiments on a hardware platform, and present it in a short written report and in an oral presentation.

The projects are typically performed using either of two hardware platforms, a mini-drone called CrazyFlie, or a modified RC-car equipped with Lidars, a camera, an IMU, Raspberry-Pi, and Arduino boards.

crazyflieRC car To obtain a grade of 4 or 5, it is also required to complete additional smaller hand-in exercises, widening the scope of selected parts of the course or going deeper into selected theoretical aspects of the course.

Course content

Lecture and exercise plan

This is a static page, for links to reading material, lecture notes, and other course material, see the public git repository at https://gitlab.liu.se/vehsys/tsfs12.

Lecture  Topic
Lec 1 Introduction
Lec 2 Discrete motion planning
Hand-in 1 Discrete motion planning
Lec 3 Modelling of ground vehicles
Lec 4 Planning with differential constraints
Hand-in 2 Planning with differential constraints
Lec 5 Optimization for motion planning
Lec 6 Control of autonomous vehicles I
Lec 6 Model predictive control for autonomous vehicles
Hand-in 3 Path-following for autonomous vehicles
Lec 8 Control of autonomous vehicles II
Lec 9 Collaborative control
Hand-in 4 Collaborative control
Lec 10 Learning for autonomous vehicles I
Hand-in 5 Learning for autonomous vehicles
Proj Introduction to mini-project
Lec 11 Learning for autonomous vehicles II and guest lecture
Proj Project seminar