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Self-Driving Car Engineer - Path Planning

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.

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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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Reviews summary

Practical self-driving path planning

According to students, this "Self-Driving Car Engineer - Path Planning" course offers a largely positive and highly practical learning experience crucial for autonomous vehicle development. Learners consistently commend its well-structured content, challenging hands-on projects that provide real-world application, and the invaluable industry insights from Mercedes-Benz R&D experts. While generally seen as demanding but rewarding, a notable point is the strong reliance on pre-existing C++ proficiency; some learners found prerequisites understated, leading to a steep learning curve. Recent feedback suggests improvements in support and environment setup, indicating the course is continuously refined.

Well-structured content and deep dives into complex algorithms.

"This course is incredibly well-structured, making complex path planning concepts... understandable."

"A solid course covering the core aspects of path planning... clear breakdown of topics."

"The best module in the Nanodegree for me. It dives deep into complex algorithms like behavioral planning and trajectory generation."

Valuable real-world perspectives from Mercedes-Benz R&D experts.

"Excellent material from Mercedes-Benz R&D. The insights into real-world challenges and industry practices are invaluable."

"The real-world relevance is huge, especially with the Mercedes-Benz involvement."

"The instructors provide a deep dive into path planning, appreciated real-world scenarios and emphasis on robust code."

Hands-on projects offer real-world application and solidify learning.

"The projects are challenging but highly practical, providing hands-on experience that is directly applicable to real-world self-driving car development."

"The hands-on projects, particularly the highway driving project, were the highlight for me, truly solidifying my understanding."

"This is exactly what I needed to advance my skills in self-driving car engineering, with projects that make you apply what you learn immediately."

Some sections might feel rushed or require more detailed guidance.

"My only minor gripe is that some parts felt a bit rushed, especially the environmental prediction module."

"Some explanations were a bit high-level without enough practical walk-throughs for beginners."

"The projects were too open-ended for me, and I felt like I was constantly guessing."

Earlier reviews cited difficulties with environment setup and dependencies.

"I spent more time debugging setup issues and understanding boilerplate code than learning path planning."

"The initial setup process for the project environment was cumbersome, and there were some outdated library warnings."

"The setup process and ensuring all dependencies worked was a major hurdle for me."

Requires strong C++ skills; can be challenging without them.

"Make sure your C++ skills are strong before starting."

"If your C++ isn't rock solid, you will struggle significantly, especially with the project implementations."

"I struggled a lot with this course. The C++ parts were particularly difficult for me without expert C++ development knowledge."

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

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Gain a comprehensive understanding of the fundamental concepts and algorithms in path planning from an authoritative source.

View Virtual Reality on Amazon

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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

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Recall key concepts and techniques from calculus and differential equations to strengthen your mathematical foundation for path planning algorithms.

Browse courses on Calculus

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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

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Connect with fellow learners, share insights, and clarify concepts through active discussions and knowledge exchange.

Browse courses on Collaborative Learning

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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

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Watch Tutorials on Path Planning Algorithms

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Gain a practical understanding of different path planning algorithms and their implementation techniques.

Browse courses on Path Planning

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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

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Hone your coding skills and deepen your understanding of path planning algorithms by solving challenging coding problems.

Browse courses on Algorithm Optimization

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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

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Specialize your knowledge by applying path planning techniques to a specific domain, such as robotics, transportation, or gaming.

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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 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.

See salaries and explore the career path for Automotive 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.

See salaries and explore the career path for Software 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.

See salaries and explore the career path for Systems 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.

See salaries and explore the career path for Sensor 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.

See salaries and explore the career path for Robotics 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.

See salaries and explore the career path for Electrical 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.

See salaries and explore the career path for Computer Vision 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.

See salaries and explore the career path for Mechanical Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Transportation 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.

See salaries and explore the career path for Safety 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.

See salaries and explore the career path for Validation 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.

See salaries and explore the career path for Simulation Engineer

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.

See salaries and explore the career path for Technical 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.

See salaries and explore the career path for Product Manager

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.

Virtual Reality

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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.

Probabilistic Robotics

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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.

Probabilistic Robotics (Intelligent Robotics and...

Hardcover

$$$

Probabilistic Robotics (Intelligent Robotics and...

Kindle Edition

$$$

Robot Motion Planning

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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.

Robot Motion Planning (The Springer International...

Paperback

$$$$

Robot Motion Planning (The Springer International...

Kindle Edition

$$$$

Autonomous Driving

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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.

Autonomous Driving: Technical, Legal and Social...

Kindle Edition

Check Price

Introduction to Autonomous Mobile Robots, second...

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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.

Introduction to Autonomous Mobile Robots, second...

Kindle Edition

Optimal Control

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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.

Optimal Control: Linear Quadratic Methods (Dover...

Paperback

Optimal Control: Linear Quadratic Methods (Dover...

Kindle Edition

MATLAB Deep Learning

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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.

MATLAB Deep Learning: With Machine Learning, Neural...

Paperback

MATLAB Deep Learning: With Machine Learning, Neural...

Paperback

MATLAB Deep Learning: With Machine Learning, Neural...

Kindle Edition

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