edX
edXDecision-Making for Autonomous Systems
62 studentsADVANCEDEnglish
Korshub's take
Mathematical Models for Autonomous Driving
Self-driving cars face complex, real-world decision-making problems—from a single vehicle's path to coordinating fleets. This course grounds those challenges in rigorous mathematical frameworks: Markov decision processes, reinforcement learning, and event-based modeling. Taught by Chalmers, an engineering school embedded in industry partnerships, the material stays rooted in practical problems engineers actually solve.
Ideal for those with a bachelor's degree or automotive professionals looking to deepen their theoretical foundation. You'll build the mental models that transform autonomous-driving problems into solvable mathematical puzzles.
About this course
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.
Price
$249
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