Decision-Making for Autonomous Systems

Overview

In the realm of autonomous vehicles, such as self-driving cars, a myriad of complex decision-making challenges arises. These range from the solitary journey of a single vehicle to the intricate coordination required among several vehicles.

This comprehensive course delves into the fundamental mathematical models that underpin many of these real-world challenges.

Major topics covered include the Markov decision process, reinforcement learning, event-based methods, and the intricacies involved in modelling and solving decision-making problems for autonomous systems.

Geared towards individuals with a bachelor's degree or engineers working within the automotive sector who seek to enhance their understanding of decision-making models for autonomous systems, this course offers a deep dive into the subject matter.

By participating in this course, learners have the opportunity to augment their decision-making capabilities in automotive engineering, with insights from Chalmers - a premier engineering institution renowned for its significant industry connections.

Provider:

edX. Categories:

Reinforcement Learning Courses, Autonomous Vehicles Courses.

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Taught by

Samuel Jia Qing-Shan

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Subjects