OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
edX logo

Decision-Making for Autonomous Systems

Jonas Sjöberg

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.

Read more

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.

Enroll now

What's inside

Learning objectives

  • 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

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches Markov decision process (MDP), which is standard in modeling decision-making
Develops reinforcement learning and event-based methods, which are core skills for autonomous systems
Taught by Jonas Sjöberg, who is recognized for their work in autonomous systems
Examines decision-making for autonomous systems, which is highly relevant to automotive engineering
Offered by Chalmers, one of the top engineering schools that distinguished through its close collaboration with industry
Advisable for engineers in the automotive industry who need to develop their knowledge in decision-making models for autonomous systems

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Robust foundation in autonomous decision-making

According to students, this course provides a strong theoretical foundation in decision-making for autonomous systems, particularly emphasizing the Markov Decision Process (MDP) and reinforcement learning. Learners praise its relevance for automotive engineering professionals seeking to deepen their understanding of complex vehicle coordination. While the mathematical rigor is a positive for those with a suitable background, some learners found the course challenging without prior knowledge in advanced mathematics or control systems. The course is seen as highly valuable for applying fundamental models to real-world autonomous challenges, making it a pivotal step for career development.
Good balance, but could use more hands-on.
"While the theory was excellent, I would have loved more coding exercises or simulations to bridge the gap to practice."
"The balance between rigorous mathematical theory and practical insights into autonomous systems was quite good."
"I felt the theoretical concepts were well explained, setting the stage for real-world problem-solving, though practical implementation details were sometimes sparse."
Clear explanations of complex topics.
"The instructor did an amazing job breaking down very complex topics into understandable segments."
"I found the lectures engaging and well-structured, making abstract concepts accessible."
"My understanding significantly improved due to the instructor's clear and concise explanations."
Highly applicable for automotive engineers.
"As an engineer in the automotive industry, I found the real-world applications discussed incredibly pertinent to my work."
"The examples provided resonated with actual challenges in self-driving cars, making the learning highly practical."
"I can directly apply the decision-making models to my projects in autonomous vehicle development."
Provides a solid grounding in core mathematical models.
"The course delves deep into the mathematical frameworks, which is exactly what I needed to grasp MDP and reinforcement learning."
"I appreciate how thoroughly it covered the fundamental concepts; it really solidified my understanding of decision-making algorithms."
"This course gave me a robust theoretical base to approach complex autonomous system problems effectively."
Assumes strong background in math and systems.
"Without a solid background in linear algebra and probability, I struggled to keep up with some of the derivations."
"I found the pace quite challenging, especially if you're not already comfortable with advanced mathematical concepts."
"This course is definitely geared towards those with a strong engineering or scientific bachelor's degree foundation."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Decision-Making for Autonomous Systems with these activities:
Review Calculus
Establish a strong foundation in Calculus.
Browse courses on Calculus
Show steps
  • Review concepts of limits, derivatives, and integrals.
  • Practice solving differential equations.
  • Work through practice problems.
Participate in Group Discussions on Autonomous Vehicle Decision-Making
Engage with peers to share insights and perspectives on decision-making for autonomous vehicles.
Browse courses on Decision-Making
Show steps
  • Join or form a study group with other learners.
  • Identify discussion topics related to decision-making in autonomous vehicles.
  • Participate actively in discussions, sharing your own thoughts and listening to others.
Follow Reinforcement Learning Tutorials
Build a practical understanding of Reinforcement Learning and its applications.
Browse courses on Reinforcement Learning
Show steps
  • Find online tutorials or courses on Reinforcement Learning.
  • Work through the tutorials and complete the exercises.
  • Apply the concepts to solve real-world problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Markov Decision Process Practice
Develop proficiency in solving Markov Decision Process problems.
Show steps
  • Solve practice problems on Markov Decision Processes.
  • Implement Markov Decision Process algorithms in a programming language.
  • Analyze the performance of your solutions.
Build an Autonomous Vehicle Simulation Environment
Gain hands-on experience by creating your own autonomous vehicle simulation environment from scratch.
Show steps
  • Research and identify the required components for building a simulation environment.
  • Design and develop the simulation environment using appropriate software tools.
  • Test and refine the simulation environment to ensure accurate and realistic behavior.
Participate in an Autonomous Vehicle Design Competition
Put your skills to the test and gain practical experience by participating in an autonomous vehicle design competition.
Browse courses on Project-Based Learning
Show steps
  • Identify an autonomous vehicle design competition that aligns with your interests.
  • Form a team and collaborate on designing and building your autonomous vehicle.
  • Test and evaluate your autonomous vehicle against other teams' designs.
Develop a Simulation Model for an Autonomous Driving System
Apply your knowledge to a real-world problem by building a simulation model of an autonomous driving system.
Browse courses on Autonomous Driving
Show steps
  • Define the scope and requirements of the simulation model.
  • Design and implement the simulation model.
  • Validate and evaluate the performance of the simulation model.
Write a Comprehensive Guide to Markov Decision Processes for Autonomous Vehicles
Deepen your understanding of Markov Decision Processes and their application in autonomous vehicles by creating a comprehensive guide.
Show steps
  • Research and gather information on Markov Decision Processes and their use in autonomous vehicles.
  • Organize and structure the guide in a logical and coherent manner.
  • Write clear and concise explanations, providing examples and illustrations.
  • Proofread and edit the guide to ensure accuracy and clarity.

Career center

Learners who complete Decision-Making for Autonomous Systems will develop knowledge and skills that may be useful to these careers:
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers help design, build, and test self-driving cars and other autonomous vehicles. This course will help you to develop the skills you need to succeed in this role by providing you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods. These are all essential concepts for designing and implementing decision-making algorithms for autonomous vehicles. The skills you gain in this course will make you a highly sought-after candidate for this exciting and growing field.
See salaries and explore the career path for Autonomous Vehicle Engineer
Robotics Engineer
Robotics Engineers deal with the design, construction, operation, and application of robots. If you are interested in designing robots that can make decisions on their own, then this course can provide you with the mathematical framework and tools you need. Through learning Markov Decision Process (MDP), reinforcement learning, and event-based methods, you will be able to model and solve decision-making problems that are common in robotics applications.
See salaries and explore the career path for Robotics Engineer
Data Scientist
Data Scientists analyze and interpret data to extract meaningful insights and help organizations make better decisions. This course can provide you with the skills you need to succeed in this role by building a strong foundation in Markov decision process, reinforcement learning, and event-based methods. These are essential concepts for developing data-driven decision-making models.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve a variety of problems. This course will help you to develop the skills you need to succeed in this role by providing you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods. These are all essential concepts for designing and implementing machine learning algorithms.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who are interested in working on self-driving cars or other autonomous systems. The course will provide you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods, which are all essential concepts for designing and implementing decision-making algorithms for autonomous systems.
See salaries and explore the career path for Software Engineer
Systems Engineer
Systems Engineers design, develop, and maintain complex systems. This course may be useful for Systems Engineers who are interested in working on self-driving cars or other autonomous systems. The course will provide you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods, which are all essential concepts for designing and implementing decision-making algorithms for autonomous systems.
See salaries and explore the career path for Systems Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems. This course may be useful for Electrical Engineers who are interested in working on self-driving cars or other autonomous systems. The course will provide you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods, which are all essential concepts for designing and implementing decision-making algorithms for autonomous systems.
See salaries and explore the career path for Electrical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain mechanical systems. This course may be useful for Mechanical Engineers who are interested in working on self-driving cars or other autonomous systems. The course will provide you with a solid foundation in Markov decision process, reinforcement learning, and event-based methods, which are all essential concepts for designing and implementing decision-making algorithms for autonomous systems.
See salaries and explore the career path for Mechanical Engineer
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries.
See salaries and explore the career path for Operations Research Analyst
Management Consultant
Management Consultants provide advice to organizations on how to improve their performance.
See salaries and explore the career path for Management Consultant
Financial Analyst
Financial Analysts analyze and interpret financial data to help organizations make better decisions.
See salaries and explore the career path for Financial Analyst
Business Analyst
Business Analysts analyze and improve business processes.
See salaries and explore the career path for Business Analyst
Data Analyst
Data Analysts analyze and interpret data to help organizations make better decisions.
See salaries and explore the career path for Data Analyst
Statistician
Statisticians collect, analyze, and interpret data to help organizations make better decisions.
See salaries and explore the career path for Statistician
Actuary
Actuaries analyze and interpret data to help organizations make better decisions about risk.
See salaries and explore the career path for Actuary

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Decision-Making for Autonomous Systems.
Cover image
Reinforcement Learning, second edition
Save
Classic introduction to reinforcement learning, a powerful machine learning technique that is used to train agents to make decisions in complex environments. It valuable resource for anyone interested in learning more about reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Introduction to Autonomous Mobile Robots, second...
Save
Provides a comprehensive overview of autonomous mobile robots, covering topics such as navigation, mapping, and localization. It valuable resource for anyone interested in learning more about autonomous mobile robots.
Introduction to Autonomous Mobile Robots, second...
Kindle Edition
$$
Cover image
Virtual Reality
Save
Provides a comprehensive overview of planning algorithms, which are used to solve problems in a variety of domains, including autonomous driving. It valuable resource for anyone interested in learning more about planning algorithms.
Virtual Reality
Hardcover
$$$
Virtual Reality
Kindle Edition
$$$
Cover image
Vehicle Dynamics and Control
Save
Provides a comprehensive overview of vehicle dynamics and control. It valuable resource for anyone interested in learning more about vehicle dynamics and control.
Vehicle Dynamics and Control (Mechanical...
Hardcover
$$$$
Vehicle Dynamics and Control (Mechanical...
Hardcover
$$$$
Vehicle Dynamics and Control by Rajamani, Rajesh...
Hardcover
$$$$
Vehicle Dynamics and Control (Mechanical...
Kindle Edition
$$$$
Vehicle Dynamics and Control (Mechanical...
Kindle Edition
$$$$
Cover image
Electric and Hybrid Vehicles
Save
Provides a comprehensive overview of automotive control systems. It valuable resource for anyone interested in learning more about automotive control systems.
Electric and Hybrid Vehicles
Paperback
Check
Electric and Hybrid Vehicles
Kindle Edition
Check
Cover image
The Global Carbon Cycle and Climate Change
Save
Provides a comprehensive overview of sustainable automotive technologies. It valuable resource for anyone interested in learning more about sustainable automotive technologies.
The Global Carbon Cycle and Climate Change
Paperback
Check
The Global Carbon Cycle and Climate Change
Kindle Edition
Check

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Decision-Making for Autonomous Systems.
Fundamentals of Reinforcement Learning
Most relevant
Sample-based Learning Methods
Most relevant
Machine Teaching for Autonomous AI
Most relevant
Designing Autonomous AI
Most relevant
Machine Learning: an overview
Most relevant
Advanced Neural Network Techniques
Most relevant
Overview of Advanced Methods of Reinforcement Learning in...
Most relevant
Modeling of Autonomous Systems
Most relevant
Autonomous Aerospace Systems
Most relevant

edX Buyer Protection & Guarantee

Verified partner policies & purchase protections

World-Class University Partner
Free Trial & Audit

Free Audit Track Available

Free audit track available for most courses, giving open access to video lectures, course readings, and assignments at no charge.

Return & Refund Policy

14-Day Full Refund Window

14-day full refund available up to 14 days after course start or 14 days after payment (whichever comes second, up to 6 months max), provided verified certificate has not yet been earned.

Buyer Protections

Verified Academic & Industry Partner

University-backed credentials authored by elite institutions like Harvard, MIT, and Oxford, with tamper-proof certificate verification.

Key Purchase Protections
Free audit track for open learning access
14-day money-back guarantee on verified certificates
Prestigious university credentials (Harvard, MIT)
Automatic unenrollment refund process via dashboard
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Effort
10 to 20 hours per week for 7 weeks
Level
Advanced
Via
edX
Institution
Chalmers University of Technology
Instructor
Jonas Sjöberg
Language
English
Transcript
English
This course belongs to a collection called Emerging Automotive Technologies. It's comprised of seven courses:
  1. Electric and Conventional Vehicles
  2. Hybrid Vehicles
  3. Road Traffic Safety in Automotive Engineering
  4. Model-Based Automotive Systems Engineering
  5. Sensor Fusion and Non-linear Filtering for Automotive Systems
  6. Multi-Object Tracking for Automotive Systems
  7. Decision-Making for Autonomous Systems (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches Markov decision process (MDP), which is standard in modeling decision-making
Develops reinforcement learning and event-based methods, which are core skills for autonomous systems
Taught by Jonas Sjöberg, who is recognized for their work in autonomous systems
Examines decision-making for autonomous systems, which is highly relevant to automotive engineering
Offered by Chalmers, one of the top engineering schools that distinguished through its close collaboration with industry
Advisable for engineers in the automotive industry who need to develop their knowledge in decision-making models for autonomous systems
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Decision-Making for Autonomous Systems.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser