Autonomous Vehicles - Planning, Control, and Learning Systems

Aim

To 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, the student should be able to understand, implement and/or use methods for

• discrete and continuous planning for autonomous vehicles, with and without motion models
• control of autonomous vehicles
• learning systems for autonomous systems

Content

• Introduction to autonomous systems and vehicles; identification of possibilities and challenges.
• Common system architectures in autonomous decision making, machine learning, planning, and control.
• Dynamic models for planning and control of autonomous vehicles.
• Fundamental planning algorithms in graphs and trees for motion of simple robots.
• Advanced algorithms for motion planning for non-holonomic vehicles described by dynamic motion equations with differential constraints.
• Introduction to and use of methods for simultaneous localization and mapping for autonomous vehicles.
• Control of autonomous vehicles; path following, model predictive control (MPC), and control of path velocity.
• Learning systems within autonomous vehicles: reinforcement learning, machine learning using deep neural networks, and Markov decision processes (MDP).
• Cooperating autonomous vehicles, including ground vehicles and flying vehicles, and the required communication.