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Lars Hammarstrand
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.
Read more
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.
Read more
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.
The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox.
The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.
Register for this course and see more details by visiting:
OpenCourser.com/course/iu3ld3/sensor
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What's inside
Learning objectives
- Basics of bayesian statistics and recursive estimation theory
- Describe and model common sensors, and their measurements
- Compare typical motion models used for positioning, in order to know when to use them in practical problems
- Describe the essential properties of the kalman filter (kf) and apply it on linear state space models
- Implement key nonlinear filters in matlab, in order to solve problems with nonlinear motion and/or sensor models
- Select a suitable filter method by analysing the properties and requirements in an application
- Basics of bayesian statistics and recursive estimation theory
- Describe and model common sensors, and their measurements
- Compare typical motion models used for positioning, in order to know when to use them in practical problems
- Describe the essential properties of the kalman filter (kf) and apply it on linear state space models
- Implement key nonlinear filters in matlab, in order to solve problems with nonlinear motion and/or sensor models
- Select a suitable filter method by analysing the properties and requirements in an application
Syllabus
Section 1 - Introduction and Primer in statisticsSection 2 - Bayesian statistics (Week 1)Section 3 - State space models and optimal filters (Week 1)Section 4 - The Kalman filter and its properties (Week 2-3)Section 5 - Motion and measurements models (Week 2-3)Section 6 - Non-linear filtering (Week 4)Section 7 - Particle filter (Week 5)
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers vital sensor fusion theories and models
Teaches advanced Bayesian techniques for sensor data fusion
Provides Python-based implementation of nonlinear filters
Taught by experienced instructor Lars Hammarstrand
Assumes learner familiarity with Bayesian statistics and basic linear algebra
Assignments require learners to implement key filtering algorithms
Save this course
Save Sensor Fusion and Non-linear Filtering for Automotive Systems 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 Sensor Fusion and Non-linear Filtering for Automotive Systems with these
activities:
Review of Linear Algebra and Calculus
Show steps
Brush up on your knowledge of linear algebra and calculus to strengthen your foundation for understanding sensor fusion.
Browse courses on
Linear Algebra
Show steps
-
Review textbooks or online resources on linear algebra.
-
Practice solving basic linear algebra problems.
-
Review the fundamentals of calculus, including derivatives and integrals.
Bayesian Statistics Problem Set 1
Show steps
Solve practice problems to reinforce your understanding of Bayesian statistics.
Browse courses on
Bayesian Statistics
Show steps
-
Review the course materials on Bayesian statistics.
-
Attempt the problem set questions.
-
Check your answers against the solutions provided.
Kalman Filter Simulations
Show steps
Implement the Kalman filter in Matlab simulations to solidify your understanding of its application in state space models.
Browse courses on
Kalman Filter
Show steps
-
Set up a simulation environment in Matlab.
-
Implement the Kalman filter algorithm.
-
Simulate object motion and sensor measurements.
-
Apply the Kalman filter to estimate the object's state.
-
Analyze the filter's performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Sensor Fusion Discussion Group
Show steps
Engage in peer discussions to explore different approaches and applications of sensor fusion in object positioning.
Browse courses on
Sensor Fusion
Show steps
-
Join or form a study group with classmates.
-
Choose a topic related to sensor fusion.
-
Facilitate discussions and share insights.
Contribute to Open Source Sensor Fusion Projects
Show steps
Contribute to open-source projects related to sensor fusion to gain hands-on experience and connect with the community.
Browse courses on
Sensor Fusion
Show steps
-
Explore open-source sensor fusion projects on platforms like GitHub.
-
Identify areas where you can make meaningful contributions.
-
Submit pull requests or create new issues to participate in the project.
Sensor Fusion Tutorial
Show steps
Create a tutorial or presentation to explain the concepts and applications of sensor fusion in autonomous vehicles.
Browse courses on
Sensor Fusion
Show steps
-
Research sensor fusion techniques and applications.
-
Develop a clear and concise presentation outline.
-
Create visual aids, such as diagrams and simulations.
-
Present your tutorial to classmates or a wider audience.
Object Tracking Project
Show steps
Design and implement an object tracking system using sensor fusion techniques.
Browse courses on
Object Tracking
Show steps
-
Identify a suitable object tracking application.
-
Select and integrate sensors for data acquisition.
-
Develop a sensor fusion algorithm for object tracking.
-
Evaluate the performance of your tracking system.
Sensor Fusion White Paper
Show steps
Write a comprehensive white paper on the current state-of-the-art in sensor fusion for autonomous vehicle applications.
Browse courses on
Sensor Fusion
Show steps
-
Conduct a thorough literature review on sensor fusion techniques.
-
Analyze and compare different approaches.
-
Identify emerging trends and future research directions.
-
Synthesize your findings into a well-written white paper.
Review of Linear Algebra and Calculus
Show steps
Brush up on your knowledge of linear algebra and calculus to strengthen your foundation for understanding sensor fusion.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review textbooks or online resources on linear algebra.
- Practice solving basic linear algebra problems.
- Review the fundamentals of calculus, including derivatives and integrals.
Bayesian Statistics Problem Set 1
Show steps
Solve practice problems to reinforce your understanding of Bayesian statistics.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Review the course materials on Bayesian statistics.
- Attempt the problem set questions.
- Check your answers against the solutions provided.
Kalman Filter Simulations
Show steps
Implement the Kalman filter in Matlab simulations to solidify your understanding of its application in state space models.
Browse courses on
Kalman Filter
Show steps
Show steps
Show steps
- Set up a simulation environment in Matlab.
- Implement the Kalman filter algorithm.
- Simulate object motion and sensor measurements.
- Apply the Kalman filter to estimate the object's state.
- Analyze the filter's performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Sensor Fusion Discussion Group
Show steps
Engage in peer discussions to explore different approaches and applications of sensor fusion in object positioning.
Browse courses on
Sensor Fusion
Show steps
Show steps
Show steps
- Join or form a study group with classmates.
- Choose a topic related to sensor fusion.
- Facilitate discussions and share insights.
Contribute to Open Source Sensor Fusion Projects
Show steps
Contribute to open-source projects related to sensor fusion to gain hands-on experience and connect with the community.
Browse courses on
Sensor Fusion
Show steps
Show steps
Show steps
- Explore open-source sensor fusion projects on platforms like GitHub.
- Identify areas where you can make meaningful contributions.
- Submit pull requests or create new issues to participate in the project.
Sensor Fusion Tutorial
Show steps
Create a tutorial or presentation to explain the concepts and applications of sensor fusion in autonomous vehicles.
Browse courses on
Sensor Fusion
Show steps
Show steps
Show steps
- Research sensor fusion techniques and applications.
- Develop a clear and concise presentation outline.
- Create visual aids, such as diagrams and simulations.
- Present your tutorial to classmates or a wider audience.
Object Tracking Project
Show steps
Design and implement an object tracking system using sensor fusion techniques.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Identify a suitable object tracking application.
- Select and integrate sensors for data acquisition.
- Develop a sensor fusion algorithm for object tracking.
- Evaluate the performance of your tracking system.
Sensor Fusion White Paper
Show steps
Write a comprehensive white paper on the current state-of-the-art in sensor fusion for autonomous vehicle applications.
Browse courses on
Sensor Fusion
Show steps
Show steps
Show steps
- Conduct a thorough literature review on sensor fusion techniques.
- Analyze and compare different approaches.
- Identify emerging trends and future research directions.
- Synthesize your findings into a well-written white paper.
Career center
Learners who complete Sensor Fusion and Non-linear Filtering for Automotive Systems will develop knowledge and skills
that may be useful to these careers:
Automotive Engineer
Automotive Engineer
Automotive Engineers design, develop, and test vehicles. They use their knowledge of mechanical engineering, electrical engineering, and computer science to develop vehicles that meet the needs of consumers and regulators. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in automotive engineering, which is essential for developing safe and efficient vehicles.
Aerospace Engineer
Aerospace Engineer
Aerospace Engineers design, develop, and test aircraft, spacecraft, and other aerospace vehicles. They use their knowledge of aerodynamics, propulsion, and control systems to develop vehicles that can fly safely and efficiently. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in aerospace engineering, which is essential for developing safe and reliable aerospace vehicles.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. They use their expertise in machine learning algorithms, data science, and software engineering to build models that can learn from data and make predictions. The course Sensor Fusion and Non-linear Filtering for Automotive Systems covers essential techniques for working with nonlinear data, which is common in many machine learning applications. By taking this course, students will gain the skills and knowledge needed to develop more robust and accurate machine learning models.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots for a variety of applications, such as manufacturing, healthcare, and space exploration. They use their knowledge of robotics, control systems, and computer science to develop robots that can perform complex tasks. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in robotics engineering, which is essential for developing robots that can navigate and interact with the world around them.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design and implement control systems for a variety of applications, such as robotics, self-driving cars, and industrial automation. They use their knowledge of control theory and systems engineering to develop systems that can achieve desired performance objectives. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in control systems engineering, which is essential for developing safe and reliable control systems.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design and develop mechanical systems for a variety of applications, such as manufacturing, transportation, and energy. They use their knowledge of mechanics, materials science, and thermodynamics to develop systems that can meet the needs of users and stakeholders. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in mechanical engineering, which is essential for developing safe and efficient mechanical systems.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret large amounts of data to develop actionable insights that drive business decisions. They use statistical models and machine learning techniques to uncover patterns and insights from data. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides a solid foundation in Bayesian statistics, which is a cornerstone of data science and machine learning. By understanding Bayesian statistics, students can develop more accurate and reliable models for data analysis.
Systems Engineer
Systems Engineer
Systems Engineers are responsible for designing, developing, and integrating complex systems. They use their knowledge of systems engineering principles and practices to ensure that systems meet the needs of users and stakeholders. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a solid understanding of the techniques and tools used in systems engineering, which can be applied to a wide range of industries.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development principles to create software that meets the needs of users. The course Sensor Fusion and Non-linear Filtering for Automotive Systems teaches students how to implement key nonlinear filters in Matlab, a valuable skill for Software Engineers who work on developing software for self-driving cars and other autonomous systems.
Electrical Engineer
Electrical Engineer
Electrical Engineers design and develop electrical systems for a variety of applications, such as power generation, distribution, and control. They use their knowledge of electrical engineering principles and practices to develop systems that meet the needs of users and stakeholders. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in electrical engineering, which is essential for developing safe and reliable electrical systems.
Computer Engineer
Computer Engineer
Computer Engineers design and develop computer hardware and software systems. They use their knowledge of computer science, electrical engineering, and software engineering to develop systems that meet the needs of users and stakeholders. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the techniques and tools used in computer engineering, which is essential for developing safe and reliable computer systems.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They use statistical techniques to develop models and theories that can be used to understand and predict the world around us. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the statistical techniques used in a variety of fields, which can be used to solve complex problems in a variety of domains.
Data Analyst
Data Analyst
Data Analysts implement data analysis techniques to help answer questions about data. They use tools and methods to convert raw data into meaningful insights and reports for an organization. By taking the course Sensor Fusion and Non-linear Filtering for Automotive Systems, students will gain essential understanding of the Bayesian statistics and recursive estimation theory, which are fundamental concepts for data analysis.
Mathematician
Mathematician
Mathematicians use their knowledge of mathematics to solve problems in a variety of fields, such as science, engineering, and finance. They use mathematical techniques to develop models and theories that can be used to understand and predict the world around us. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the mathematical techniques used in a variety of fields, which can be used to solve complex problems in a variety of domains.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and finance to assess and manage risk. They use actuarial techniques to develop models and theories that can be used to understand and predict the financial impact of future events. The course Sensor Fusion and Non-linear Filtering for Automotive Systems provides students with a strong foundation in the mathematical and statistical techniques used in actuarial science, which can be used to solve complex problems in the insurance and finance industries.
For more career information including salaries, visit:
OpenCourser.com/course/iu3ld3/sensor
Reading list
We've selected ten 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
Sensor Fusion and Non-linear Filtering for Automotive Systems.
Provides a comprehensive treatment of state estimation for robotics, which key topic in automotive systems. It covers the underlying theory as well as practical implementation details.
Provides a comprehensive introduction to nonlinear estimation, which fundamental technique used in sensor fusion. It useful reference for understanding the theoretical foundations of sensor fusion and for gaining a deeper understanding of the algorithms used in the course.
Save
Provides a clear and concise introduction to Bayesian statistics, which is the foundation of sensor fusion algorithms. It covers the basics of probability theory, Bayes' theorem, and Bayesian inference.
Provides a clear and concise introduction to applied Bayesian statistics, which fundamental topic in sensor fusion. It covers the basics of Bayesian statistics as well as practical implementation details.
Provides a comprehensive introduction to probabilistic robotics, which fundamental technique used in sensor fusion and autonomous navigation. It useful reference for understanding the theoretical foundations of sensor fusion and for gaining a deeper understanding of the algorithms used in the course.
Provides a comprehensive treatment of optimal estimation theory, which is the foundation for Kalman filtering and sensor fusion. It useful reference for understanding the theoretical foundations of sensor fusion and for gaining a deeper understanding of the algorithms used in the course.
Provides a comprehensive treatment of nonlinear filtering, which fundamental topic in sensor fusion. It covers the underlying theory as well as practical implementation details.
Provides a comprehensive treatment of statistical signal processing, which fundamental topic in sensor fusion. It covers a wide range of topics, including estimation, detection, and adaptive filtering.
Provides a comprehensive introduction to multisensor data fusion, which fundamental technique used in sensor fusion. It useful reference for understanding the theoretical foundations of sensor fusion and for gaining a deeper understanding of the algorithms used in the course.
Provides a practical introduction to the Kalman filter, with a focus on MATLAB examples. It useful resource for understanding the implementation of the Kalman filter and for gaining hands-on experience with the techniques used in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iu3ld3/sensor
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Effort
10 to 20 hours per week for 9 weeks
Level
Advanced
Via
edX
Institution
Chalmers University of Technology
Instructor
Lars Hammarstrand
Session
January 29, 2024
- January 27, 2025
Language
English
Transcript
English
This course belongs to a
collection
called
Emerging Automotive Technologies. It's comprised of seven courses:
- Electric and Conventional Vehicles
- Hybrid Vehicles
- Road Traffic Safety in Automotive Engineering
- Model-Based Automotive Systems Engineering
- Sensor Fusion and Non-linear Filtering for Automotive Systems (viewing)
- Multi-Object Tracking for Automotive Systems
- Decision-Making for Autonomous Systems
Good to know
Covers vital sensor fusion theories and models
Teaches advanced Bayesian techniques for sensor data fusion
Provides Python-based implementation of nonlinear filters
Taught by experienced instructor Lars Hammarstrand
Assumes learner familiarity with Bayesian statistics and basic linear algebra
Assignments require learners to implement key filtering algorithms
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