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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/obn5fv/udacity
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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
Show steps
Brush up on basic calculus to strengthen your ability to keep pace with the mathematical concepts introduced in the course.
Browse courses on
Calculus
Show steps
-
Go through your old calculus notes or textbooks.
-
Solve practice problems to test your understanding.
Review basic physics
Show steps
Refresh your knowledge of basic physics concepts to enhance your understanding of the underlying principles of self-driving cars.
Browse courses on
Physics
Show steps
-
Review your old physics notes or textbooks.
-
Work through practice problems to reinforce your understanding.
Practice solving control theory problems
Show steps
Complete a set of exercises to reinforce the concepts of control theory covered in the course.
Browse courses on
Control Theory
Show steps
-
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
Show steps
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
Show steps
Build a model in Simulink to simulate the behavior of a self-driving car, apply concepts from the course.
Browse courses on
Simulink
Show steps
-
Design the vehicle model
-
Develop the control system
-
Simulate the model
Practice implementing control algorithms
Show steps
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
Show steps
Apply the principles of control theory to design and implement a controller for a simulated self-driving car.
Browse courses on
Controller design
Show steps
-
Define the control objectives
-
Design the controller
-
Implement the controller
-
Test the controller
Review calculus
Show steps
Brush up on basic calculus to strengthen your ability to keep pace with the mathematical concepts introduced in the course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Go through your old calculus notes or textbooks.
- Solve practice problems to test your understanding.
Review basic physics
Show steps
Refresh your knowledge of basic physics concepts to enhance your understanding of the underlying principles of self-driving cars.
Browse courses on
Physics
Show steps
Show steps
Show steps
- Review your old physics notes or textbooks.
- Work through practice problems to reinforce your understanding.
Practice solving control theory problems
Show steps
Complete a set of exercises to reinforce the concepts of control theory covered in the course.
Browse courses on
Control Theory
Show steps
Show steps
Show steps
- 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
Show steps
Engage with peers in a study group to discuss course concepts, share knowledge, and support each other's learning.
Show steps
Show steps
Show steps
- Identify potential study partners.
- Meet regularly to discuss course material.
Create a Simulink model of a self-driving car
Show steps
Build a model in Simulink to simulate the behavior of a self-driving car, apply concepts from the course.
Browse courses on
Simulink
Show steps
Show steps
Show steps
- Design the vehicle model
- Develop the control system
- Simulate the model
Practice implementing control algorithms
Show steps
Practice implementing various control algorithms to enhance your understanding of how these algorithms are used in self-driving cars.
Show steps
Show steps
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
Show steps
Apply the principles of control theory to design and implement a controller for a simulated self-driving car.
Browse courses on
Controller design
Show steps
Show steps
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:
For more career information including salaries, visit:
OpenCourser.com/course/obn5fv/udacity
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.
Save
Provides a comprehensive overview of the algorithms used in computer vision. It covers the basics of image processing, as well as more advanced topics such as object recognition and image segmentation.
Save
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/obn5fv/udacity
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Good to know
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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