Autonomous Vehicle Decision-Making
Explore advanced methodologies and algorithms essential in developing intelligent decision systems that power autonomous vehicles and self-driving technologies.
Explore advanced methodologies and algorithms essential in developing intelligent decision systems that power autonomous vehicles and self-driving technologies.
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.
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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
Skills you'll gain
This course includes:
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Graded assignments, exams
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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
Instructor
1 Course
Expert in Mechatronics and Automotive Systems at Chalmers
Professor Jonas Sjöberg, a distinguished expert in Mechatronics at Chalmers University of Technology, leads the Mechatronic research group and spearheads cutting-edge research in mechatronics, signal processing, and control. His work focuses on model-based methods, simulations, system identification, and optimization for mechatronic systems, with particular emphasis on Automotive Active Safety and Hybrid Electric Vehicles. As a professor and research group leader, Sjöberg not only conducts groundbreaking research but also shapes the next generation of engineers through his supervision of undergraduate and graduate students. His expertise extends to modeling, simulation, and control of hybrid vehicles, contributing to national projects and guiding Ph.D. students in the design, sizing, and control of electrical hybrids. Sjöberg's involvement in research on certification of heavy-duty hybrid vehicles underscores his commitment to practical applications. Through his course on "Decision-Making for Autonomous Systems," he bridges theoretical knowledge with real-world challenges in autonomous vehicle technology, cementing his role as a key figure in advancing mechatronics and automotive engineering at Chalmers.
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