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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.
Read more
This course is a part of the Self-Driving Car Engineer Nanodegree Program.
Path planning is the brains of a self-driving car. It’s how a vehicle decides how to get where it’s going, both at the macro and micro levels. You’ll learn about three core components of path planning: environmental prediction, behavioral planning, and trajectory generation. Best of all, this module is taught by our partners at Mercedes-Benz Research & Development North America. Their participation ensures that the module focuses specifically on material job candidates in this field need to know.
Register for this course and see more details by visiting:
OpenCourser.com/course/aausox/udacity
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possible dealbreakers
Develops critical skills for planning paths for autonomous vehicles, preparing learners for the industry
Taught by Mercedes-Benz Research & Development North America, industry experts in autonomous vehicle technology
Covers the core components of path planning: environmental prediction, behavioral planning, and trajectory generation
Requires knowledge of C++ and Calculus as prerequisites
Part of the Self-Driving Car Engineer Nanodegree Program, providing a comprehensive learning path in the field
Save this course
Save Self-Driving Car Engineer - Path Planning to your list so you can find it easily later:
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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 - Path Planning with these
activities:
Read 'Planning Algorithms' by Steven LaValle
Show steps
Gain a comprehensive understanding of the fundamental concepts and algorithms in path planning from an authoritative source.
View
Virtual Reality
on Amazon
Show steps
-
Obtain a copy of the book and set aside dedicated reading time.
-
Read the chapters thoroughly, taking notes and highlighting key concepts.
-
Work through the exercises and examples to reinforce your understanding.
Review Basic Calculus and Differential Equations
Show steps
Recall key concepts and techniques from calculus and differential equations to strengthen your mathematical foundation for path planning algorithms.
Browse courses on
Calculus
Show steps
-
Review textbooks and online resources covering calculus and differential equations.
-
Practice solving problems related to derivatives, integrals, and differential equations.
Join Study Groups and Discuss Concepts
Show steps
Connect with fellow learners, share insights, and clarify concepts through active discussions and knowledge exchange.
Browse courses on
Collaborative Learning
Show steps
-
Join online forums or discussion groups dedicated to path planning.
-
Participate in discussions, ask questions, and provide answers to enhance your understanding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Watch Tutorials on Path Planning Algorithms
Show steps
Gain a practical understanding of different path planning algorithms and their implementation techniques.
Browse courses on
Path Planning
Show steps
-
Identify reputable online platforms offering tutorials on path planning algorithms.
-
Follow along with video tutorials, taking notes and experimenting with code examples.
Solve Path Planning Coding Challenges
Show steps
Hone your coding skills and deepen your understanding of path planning algorithms by solving challenging coding problems.
Browse courses on
Algorithm Optimization
Show steps
-
Join online coding platforms and participate in path planning challenges.
-
Analyze problem statements, design algorithms, and implement solutions in C++.
-
Review and optimize your code to improve efficiency and accuracy.
Develop a Path Planning Algorithm for a Specific Domain
Show steps
Specialize your knowledge by applying path planning techniques to a specific domain, such as robotics, transportation, or gaming.
Show steps
-
Identify a specific domain and research the unique challenges and requirements.
-
Design and implement a path planning algorithm tailored to the domain.
-
Evaluate the performance of your algorithm through simulations or experiments.
Read 'Planning Algorithms' by Steven LaValle
Show steps
Gain a comprehensive understanding of the fundamental concepts and algorithms in path planning from an authoritative source.
View
Virtual Reality
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book and set aside dedicated reading time.
- Read the chapters thoroughly, taking notes and highlighting key concepts.
- Work through the exercises and examples to reinforce your understanding.
Review Basic Calculus and Differential Equations
Show steps
Recall key concepts and techniques from calculus and differential equations to strengthen your mathematical foundation for path planning algorithms.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review textbooks and online resources covering calculus and differential equations.
- Practice solving problems related to derivatives, integrals, and differential equations.
Join Study Groups and Discuss Concepts
Show steps
Connect with fellow learners, share insights, and clarify concepts through active discussions and knowledge exchange.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Join online forums or discussion groups dedicated to path planning.
- Participate in discussions, ask questions, and provide answers to enhance your understanding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Watch Tutorials on Path Planning Algorithms
Show steps
Gain a practical understanding of different path planning algorithms and their implementation techniques.
Browse courses on
Path Planning
Show steps
Show steps
Show steps
- Identify reputable online platforms offering tutorials on path planning algorithms.
- Follow along with video tutorials, taking notes and experimenting with code examples.
Solve Path Planning Coding Challenges
Show steps
Hone your coding skills and deepen your understanding of path planning algorithms by solving challenging coding problems.
Browse courses on
Algorithm Optimization
Show steps
Show steps
Show steps
- Join online coding platforms and participate in path planning challenges.
- Analyze problem statements, design algorithms, and implement solutions in C++.
- Review and optimize your code to improve efficiency and accuracy.
Develop a Path Planning Algorithm for a Specific Domain
Show steps
Specialize your knowledge by applying path planning techniques to a specific domain, such as robotics, transportation, or gaming.
Show steps
Show steps
Show steps
- Identify a specific domain and research the unique challenges and requirements.
- Design and implement a path planning algorithm tailored to the domain.
- Evaluate the performance of your algorithm through simulations or experiments.
Career center
Learners who complete Self-Driving Car Engineer - Path Planning will develop knowledge and skills
that may be useful to these careers:
Automotive Engineer
Automotive Engineer
Automotive Engineers design, develop, and test vehicles, including self-driving cars. This course can help build a foundation in the skills needed for this role, such as mechanical engineering, automotive engineering, and electrical engineering.
Software Engineer
Software Engineer
Software Engineers develop and maintain the software that powers self-driving cars. This course can help build a foundation in the skills needed for this role, such as computer science, software development, and algorithms.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain the mechanical systems of self-driving cars. This course can help build a foundation in the skills needed for this role, such as mechanical engineering, materials science, and manufacturing.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems for self-driving cars. This course can help build a foundation in the skills needed for this role, such as computer vision, machine learning, and image processing.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots, including those used in self-driving cars. This course can help build a foundation in the skills needed for this role, such as mechanical engineering, computer science, and electrical engineering.
Systems Engineer
Systems Engineer
Systems Engineers design and integrate the various components of self-driving cars. This course can help build a foundation in the skills needed for this role, such as systems engineering, project management, and risk management.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain the electrical systems of self-driving cars. This course can help build a foundation in the skills needed for this role, such as electrical engineering, power electronics, and control systems.
Sensor Engineer
Sensor Engineer
Sensor Engineers design, develop, and maintain the sensors used in self-driving cars. This course can help build a foundation in the skills needed for this role, such as sensor design, signal processing, and data acquisition.
Validation Engineer
Validation Engineer
Validation Engineers test and validate self-driving car systems to ensure they meet requirements. This course can help build a foundation in the skills needed for this role, such as testing, data analysis, and reporting.
Simulation Engineer
Simulation Engineer
Simulation Engineers develop and use simulations to test and validate self-driving car systems. This course can help build a foundation in the skills needed for this role, such as simulation modeling, data analysis, and visualization.
Transportation Engineer
Transportation Engineer
Transportation Engineers design and manage transportation systems, including those for self-driving cars. This course can help build a foundation in the skills needed for this role, such as transportation engineering, traffic analysis, and urban planning.
Safety Engineer
Safety Engineer
Safety Engineers ensure that self-driving cars are safe and reliable. This course can help build a foundation in the skills needed for this role, such as safety engineering, risk assessment, and quality control.
Data Scientist
Data Scientist
Data Scientists play a vital role in the development of self-driving cars by analyzing large amounts of data to identify patterns and trends. This course can help build a foundation in the skills needed for this role, such as data analysis, machine learning, and statistics.
Technical Product Manager
Technical Product Manager
Technical Product Managers oversee the development and launch of self-driving car products. This course can help build a foundation in the skills needed for this role, such as product management, marketing, and business development.
Product Manager
Product Manager
Product Managers oversee the development and launch of self-driving car products. This course may be useful for building a foundation in the skills needed for this role, such as product management, marketing, and business development.
For more career information including salaries, visit:
OpenCourser.com/course/aausox/udacity
Reading list
We've selected seven 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 - Path Planning.
Save
Provides a comprehensive overview of planning algorithms, which are essential for path planning in self-driving cars. It covers a wide range of topics, from basic concepts to advanced techniques, making it a valuable resource for both beginners and experienced researchers.
Provides a comprehensive overview of probabilistic robotics, which is essential for path planning in self-driving cars. It covers a wide range of topics, from basic concepts to advanced techniques, making it a valuable resource for both beginners and experienced researchers.
Provides a comprehensive overview of robot motion planning, which is closely related to path planning for self-driving cars. It covers a wide range of topics, from basic concepts to advanced techniques, making it a valuable resource for both beginners and experienced researchers.
Save
Provides a comprehensive overview of autonomous driving, including path planning. It covers a wide range of topics, from technical concepts to legal and social issues, making it a valuable resource for both researchers and policymakers.
Provides a comprehensive overview of autonomous mobile robots, including path planning. It is written in a clear and concise style, making it accessible to both students and researchers.
Save
Provides a comprehensive overview of optimal control and estimation, which are essential for path planning in self-driving cars. It covers a wide range of topics, from basic concepts to advanced techniques, making it a valuable resource for both beginners and experienced researchers.
Save
Provides a comprehensive overview of deep learning for self-driving cars. It covers a wide range of topics, from basic concepts to advanced techniques, making it a valuable resource for both beginners and experienced researchers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/aausox/udacity
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Good to know
Develops critical skills for planning paths for autonomous vehicles, preparing learners for the industry
Taught by Mercedes-Benz Research & Development North America, industry experts in autonomous vehicle technology
Covers the core components of path planning: environmental prediction, behavioral planning, and trajectory generation
Requires knowledge of C++ and Calculus as prerequisites
Part of the Self-Driving Car Engineer Nanodegree Program, providing a comprehensive learning path in the field
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