Sensor Fusion and Non-linear Filtering for Automotive Systems

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.

Created by: Chalmers University of Technology

Level: Advanced

Find Out More
Share
Facebook
Twitter
Pinterest
Reddit
StumbleUpon
LinkedIn
Email

Everest University-Melbourne Online Courses

Back to Top

Log In

Contact Us

Upload An Image

Please select an image to upload
Note: must be in .png, .gif or .jpg format
OR
Provide URL where image can be downloaded
Note: must be in .png, .gif or .jpg format

By clicking this button,
you agree to the terms of use

By clicking "Create Alert" I agree to the Uloop Terms of Use.

Image not available.

Add a Photo

Please select a photo to upload
Note: must be in .png, .gif or .jpg format