Autonomous Vehicle Decision-Making
Explore advanced methodologies and algorithms essential in developing intelligent decision systems that power autonomous vehicles and self-driving technologies.
Course Cost
₹ 21,145
Advanced
Skill Level
7 Weeks
Self-paced lessons
This advanced course focuses on decision-making processes for autonomous vehicles, particularly self-driving cars. It covers fundamental mathematical models used in real-world autonomous systems, including Markov decision processes, reinforcement learning, and event-based methods. Students will learn to model and solve complex decision-making problems in autonomous systems, with a focus on applications in the automotive industry. The course is designed for learners with a bachelor's degree or engineers in the automotive sector looking to expand their knowledge in autonomous system decision-making models. By learning from Chalmers, a top engineering school known for its industry collaborations, students will gain valuable insights into cutting-edge automotive engineering practices.
What you'll learn
Use Markov decision process (MDP) as a framework for modeling decision-making in autonomous systems
Understand and apply reinforcement learning techniques to autonomous vehicle scenarios
Implement event-based methods for decision-making in self-driving cars
Model complex decision-making problems for various autonomous systems
Solve real-world decision-making challenges in the context of autonomous vehicles
Apply mathematical modeling to improve autonomous system performance
Develop strategies for coordinating decisions among multiple autonomous vehicles
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course, "Decision-Making for Autonomous Systems," focuses on the complex decision-making processes involved in autonomous vehicles, particularly self-driving cars. It covers three main areas: Markov decision processes (MDP), reinforcement learning, and event-based methods. Students will learn how to apply these mathematical frameworks to model and solve real-world decision-making problems in autonomous systems. The course emphasizes both theoretical understanding and practical application, with a strong focus on the automotive industry. By the end of the course, learners will be able to use MDPs as a mathematical framework for modeling decision-making, understand and apply reinforcement learning and event-based methods, and model and solve decision-making problems for autonomous systems in various scenarios.
Fee Structure
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.




