Decision-Making for Autonomous Systems

Learn effectivetactics for making keydecisionswhen working with autonomous, self-driving vehicles.

Decision-Making for Autonomous Systems

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Estimated 6 weeks
10–20 hours per week
Instructor-paced
Instructor-led on a course schedule

About this course

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In autonomous vehicles such as self-driving cars, we find a number of interesting and challenging decision-making problems. Starting from the autonomous driving of a single vehicle, to the coordination among multiple vehicles.

This course will teach you the fundamental mathematical model for many of these real-world problems. Key topics include Markov decision process, reinforcement learning and event-based methods as well as the modelling and solving of decision-making for autonomous systems.

This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge in decision-making models for autonomous systems.

Enhance your decision-making skills in automotive engineering by learning from Chalmers, one of the top engineering schools that distinguished through its close collaboration with industry.

At a glance

What you'll learn

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  • Use Markov decision process (MDP) a mathematical framework for modellingdecision-making
  • Understand and apply reinforcement learning and event-based methods
  • Model and solve decision-making problems for autonomous systems

About the instructors

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.