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Sven Andersson, Anders Grauers, Mats Svensson, Jonas Fredriksson, Lars Hammarstrand, Lennart Svensson, Samuel Jia Qing-Shan, Giulio Bianchi Piccinini, Jonas Bärgman, András Bálint, Marco Dozza, Robert Thomson, Jonas Sjöberg, Yuxuan Xia, and Karl Granström

The automotive industry is changing in fundamental ways with the increased focus on sustainable and self-driving cars. Today, the internal combustion engine is increasingly complemented or even replaced with an electric motor. Vehicles are becoming more and more autonomous, creating an ever-increasing need for better software. Consequently, skilled automotive engineers are required to meet the demands of this new environment.

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The automotive industry is changing in fundamental ways with the increased focus on sustainable and self-driving cars. Today, the internal combustion engine is increasingly complemented or even replaced with an electric motor. Vehicles are becoming more and more autonomous, creating an ever-increasing need for better software. Consequently, skilled automotive engineers are required to meet the demands of this new environment.

In this MicroMasters program, you will learn the fundamentals of not only how a vehicle is designed, but also how to model and simulate the vehicle dynamics. Learn how to implement intelligent perception and decision procedures needed for self-driving cars and how model-based design is widely used in industry to accurately simulate the vehicle but also to design efficient algorithms.

Graduates from the MicroMasters program will be equipped with the necessary skills for a career within the automotive industry and will have a broad perspective of the emerging technologies within the development of automotive technologies.

What you'll learn

  • Design and simulate electric and hybrid powertrains
  • Understand the fundamentals of passive and active safety concepts
  • Model and simulate vehicle dynamics
  • Implement intelligent perception and decision procedures needed for self-driving cars

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What's inside

Seven courses

Electric and Conventional Vehicles

(105 hours)
Electric powertrains are estimated to propel a large part of road vehicles in the future, due to their high efficiency and zero tailpipe emissions. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about electric powertrains.

Hybrid Vehicles

(105 hours)
Why are hybrid vehicles still more common than battery electric ones? Why are electric vehicles still more expensive than conventional or hybrid ones? In this course, you will get the answers to this and much more.

Road Traffic Safety in Automotive Engineering

(63 hours)
Engineers in the automotive industry must understand basic safety concepts. This course teaches the fundamentals of active safety (systems for avoiding crashes or reducing crash consequences) as well as passive safety (systems for avoiding or reducing injuries). Key concepts include in-crash protective systems, collision avoidance, and safe automated driving.

Model-Based Automotive Systems Engineering

(105 hours)
Modeling, control design, and simulation are important tools supporting engineers in the development of automotive systems. This course provides a theoretical basis to model-based control design with the focus on systematically develop mathematical models from basic physical laws and to use them in control design process with specific focus on automotive applications.

Sensor Fusion and Non-linear Filtering for Automotive Systems

(135 hours)
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally.

Multi-Object Tracking for Automotive Systems

(150 hours)
Autonomous vehicles rely critically on an accurate perception of their environment. This course teaches the fundamentals of multi-object tracking for automotive systems, including sensors, motion models, and filters for handling varying numbers of objects.

Decision-Making for Autonomous Systems

(105 hours)
In autonomous vehicles, we find interesting and challenging decision-making problems. 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.

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