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

Vehicles use many different sensors to understand the environment. Sensor fusion uses different types of Kalman filters - mathematical algorithms - to combine data from these sensors and develop a consistent understanding of the world.

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Helps learners develop proficiency in sensor fusion, which is standard in auto industry
Teaches Kalman filters, which are foundational to data analysis in autonomous vehicle engineering
Develops knowledge of sensor fusion algorithms, which are highly relevant to autonomous vehicle engineering
May require a significant background knowledge of calculus, linear algebra, and programming
Part of a larger Nanodegree program in Self-Driving Car Engineering, which could be beneficial for students seeking a comprehensive foundation in the field

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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 - Sensor Fusion with these activities:
Review Calculus
Review the basics of calculus to ensure a strong foundation for the course.
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  • Review the concepts of limits and continuity.
  • Practice differentiation techniques.
  • Review integration techniques.
Follow Kalman Filter Tutorials
Enhance your understanding of Kalman filters by following guided tutorials.
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  • Search for tutorials on Kalman filters and sensor fusion.
  • Watch video tutorials or read articles to learn about the concepts.
  • Follow along with the tutorials to practice using Kalman filters.
Develop a Kalman Filter Visualization
Practice using visualization techniques to demonstrate your understanding of Kalman filters.
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  • Review the course materials on Kalman filters.
  • Choose a programming language and environment for your visualization.
  • Design and implement a visualization that demonstrates the operation of a Kalman filter.
  • Share your visualization with others for feedback.
Three other activities
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Design a Sensor Fusion System
Solidify your knowledge of sensor fusion by designing a system for a real-world scenario.
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  • Choose a specific vehicle type and scenario for your system.
  • Identify the sensors that will be used in your system.
  • Design the algorithm that will fuse the data from the sensors.
  • Write a report summarizing your design.
Solve Sensor Fusion Practice Problems
Reinforce your understanding of sensor fusion by completing practice problems.
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  • Find practice problems related to sensor fusion and Kalman filters.
  • Solve the problems using the knowledge you have gained in the course.
  • Review the solutions to identify areas where you need improvement.
Attend a Sensor Fusion Workshop
Gain practical insights and hands-on experience by attending a sensor fusion workshop.
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  • Research and find a reputable workshop focusing on sensor fusion.
  • Register for the workshop and attend all sessions.
  • Participate actively in discussions and exercises.

Career center

Learners who complete Self-Driving Car Engineer - Sensor Fusion will develop knowledge and skills that may be useful to these careers:
Automotive Electronics Engineer
Automotive Electronics Engineers design, develop, and test electronic systems for automobiles. They work closely with other engineers to ensure that the electronic systems are compatible with the mechanical and electrical systems of the vehicle. This course would be especially useful for those interested in learning about the use of sensors and sensor fusion in self-driving cars.
Mechatronics Engineer
Mechatronics Engineers design, develop, and test systems that combine mechanical, electrical, and computer engineering. They work with a variety of engineers to ensure that the systems are compatible with each other. This course would be especially useful for those interested in learning about the use of sensors and sensor fusion in self-driving cars.
Electrical Engineer
Electrical Engineers design, develop, and test electrical systems. They work with mechanical and computer engineers to ensure that the electrical systems are compatible with the mechanical and computer systems of the system. This course would be especially useful for those interested in learning about the use of sensors and sensor fusion in self-driving cars.
Software Engineer
Software Engineers design, develop, and test software. They work with a variety of engineers to ensure that the software is compatible with the hardware and mechanical systems of the system. This course would be especially useful for those interested in learning about the use of sensors and sensor fusion in self-driving cars.
Embedded Software Engineer
Embedded Software Engineers design, develop, and test software for embedded systems. They work with hardware engineers to ensure that the software is compatible with the hardware. This course would be especially useful for those interested in learning about the use of sensors and sensor fusion in self-driving cars.
Systems Engineer
Systems Engineers design, develop, and test systems that combine a variety of engineering disciplines. They work with a variety of engineers to ensure that the systems are compatible with each other. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Controls Engineer
Controls Engineers design, develop, and test control systems for a variety of applications. They work with mechanical and electrical engineers to ensure that the control systems are compatible with the mechanical and electrical systems of the system. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Data Scientist
Data Scientists use data to solve problems and make predictions. They work with a variety of data sources, including structured and unstructured data. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Computer Hardware Engineer
Computer Hardware Engineers design, develop, and test computer hardware. They work with software engineers to ensure that the hardware is compatible with the software. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Test Engineer
Test Engineers design, develop, and test systems to ensure that they meet the required specifications. They work with a variety of engineers to ensure that the systems are safe, functional, and efficient. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Mechanical Engineer
Mechanical Engineers design, develop, and test mechanical systems. They work with electrical and computer engineers to ensure that the mechanical systems are compatible with the electrical and computer systems of the system. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Safety Engineer
Safety Engineers design, develop, and test safety systems. They work with a variety of engineers to ensure that the safety systems are compatible with the mechanical, electrical, and computer systems of the system. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Product Design Engineer
Product Design Engineers design, develop, and test products. They work with a variety of engineers to ensure that the products are safe, functional, and aesthetically pleasing. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Aerospace Engineer
Aerospace Engineers design aircraft, spacecraft, missiles, and related components. They analyze and evaluate the performance of these systems using computer simulations and modeling. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.
Robotics Engineer
Robotics Engineers design, develop, and test robots. They work with a variety of engineers to ensure that the robots are safe, functional, and efficient. This course may be useful for those interested in learning about the use of sensors and sensor fusion in autonomous vehicles.

Reading list

We've selected 13 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 - Sensor Fusion.
Provides a comprehensive overview of autonomous vehicle technology, including sensor fusion. It valuable resource for policymakers who are developing regulations for autonomous vehicles.
Considered a classic reference text in robotics, this book provides in-depth coverage of probabilistic robotics, including sensor fusion and Kalman filtering.
Provides a comprehensive overview of the future of autonomous vehicles. It valuable resource for engineers who are developing autonomous vehicles.
Provides a comprehensive overview of sensor fusion and Kalman filtering, two essential concepts for self-driving cars, and it includes on-line resources for deeper learning.
Provides a comprehensive overview of autonomous mobile robots, including sensor fusion. It valuable resource for students and researchers who are interested in developing autonomous vehicles.
Provides a comprehensive overview of control of autonomous vehicles. It valuable resource for engineers who are developing autonomous vehicles.
Provides a comprehensive overview of the ethics of autonomous vehicles. It valuable resource for engineers who are developing autonomous vehicles.
Serves as a comprehensive reference guide on automotive sensors, providing in-depth information on their types, design, and applications in self-driving cars.
Focuses on computer vision techniques used in autonomous vehicles, providing valuable insights into how sensors are used to perceive the environment.
This textbook provides a broad overview of robotics, including chapters covering perception, sensor fusion, and motion planning, making it a foundational resource for understanding the fundamentals of self-driving cars.
Explores machine learning algorithms used in robotics, providing valuable insights into how self-driving cars learn from data and improve their performance over time.

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