Learn effective tactics for making key decisions when working with autonomous, self-driving vehicles. 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.

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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.

This course is part of the Emerging Automotive Technologies MicroMasters Program.

What you'll learn

- Use Markov decision process (MDP) a mathematical framework for modelling decision-making

- Understand and apply reinforcement learning and event-based methods

- Model and solve decision-making problems for autonomous systems

Emerging Automotive Technologies MicroMasters

Autonomous Systems

Decision Making

Markov Decision Processes

MDP

Autonomous Vehicles

Automotive Engineering

Engineering

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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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