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This course is a part of the Self-Driving Car Engineer Nanodegree Program.

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This course is a part of the Self-Driving Car Engineer Nanodegree Program.

Following a reference trajectory is a requirement for self-driving cars. The world is complex, however, so you will practice building several different types of controllers for sending control inputs to the vehicle.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides learners the experience of building controllers for self-driving cars, which involves practice in planning and executing maneuvers before vehicles can operate autonomously
Builds skills for manipulating and moving self-driving cars, a crucial component for autonomous vehicle engineering
Has a focus on self-driving car engineering which can be a valuable specialization for those interested in the automotive industry
Recommended for individuals with a background in Physics and C++

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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 Self-Driving Car Engineer - Control with these activities:
Review calculus
Brush up on basic calculus to strengthen your ability to keep pace with the mathematical concepts introduced in the course.
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Show steps
  • Go through your old calculus notes or textbooks.
  • Solve practice problems to test your understanding.
Review basic physics
Refresh your knowledge of basic physics concepts to enhance your understanding of the underlying principles of self-driving cars.
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Show steps
  • Review your old physics notes or textbooks.
  • Work through practice problems to reinforce your understanding.
Practice solving control theory problems
Complete a set of exercises to reinforce the concepts of control theory covered in the course.
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  • Review the basics of control theory
  • Solve a variety of control theory problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group for the course
Engage with peers in a study group to discuss course concepts, share knowledge, and support each other's learning.
Show steps
  • Identify potential study partners.
  • Meet regularly to discuss course material.
Create a Simulink model of a self-driving car
Build a model in Simulink to simulate the behavior of a self-driving car, apply concepts from the course.
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Show steps
  • Design the vehicle model
  • Develop the control system
  • Simulate the model
Practice implementing control algorithms
Practice implementing various control algorithms to enhance your understanding of how these algorithms are used in self-driving cars.
Show steps
  • Choose a control algorithm to implement.
  • Code the algorithm in your preferred programming language.
  • Test the algorithm in a simulated environment.
Design and implement a controller for a self-driving car
Apply the principles of control theory to design and implement a controller for a simulated self-driving car.
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Show steps
  • Define the control objectives
  • Design the controller
  • Implement the controller
  • Test the controller

Career center

Learners who complete Self-Driving Car Engineer - Control will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected eight 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 Self-Driving Car Engineer - Control.
Provides a comprehensive overview of the theory and practice of control systems design and analysis. It covers the basics of control systems, as well as more advanced topics such as state-space methods and optimal control.
Provides a comprehensive overview of the probabilistic techniques used in robotics. It covers the basics of probability theory, as well as more advanced topics such as Bayesian filtering and SLAM.
Provides a comprehensive overview of the theory and practice of reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Provides a comprehensive overview of the theory and practice of control systems engineering. It covers the basics of control systems, as well as more advanced topics such as state-space methods and optimal control.
Provides a comprehensive overview of the machine learning techniques used in robotics. It covers the basics of machine learning, as well as more advanced topics such as reinforcement learning and deep learning.
Provides a comprehensive overview of the technology behind autonomous vehicles. It covers the sensors, actuators, and algorithms used to enable cars to drive themselves. It also discusses the ethical and legal issues surrounding autonomous vehicles.
Provides a comprehensive overview of the algorithms used in robotics, vision, and control. It covers the basics of these fields, as well as more advanced topics such as Kalman filtering and computer vision.

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