Sensor Fusion and Multi-Object Tracking

Overview

Syllabus

Courses

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  9 weeks, 10-20 hours a week, 10-20 hours a week

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  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.

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  10 weeks, 10-20 hours a week, 10-20 hours a week

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  Autonomous vehicles, such as self-driving cars, rely critically on an accurate perception of their environment.

  In this course, we will teach you the fundamentals of multi-object tracking for automotive systems. Key components include the description and understanding of common sensors and motion models, principles underlying filters that can handle varying number of objects, and a selection of the main multi-object tracking (MOT) filters.

  The course builds and expands on concepts and ideas introduced in CHM013x: "Sensor fusion and nonlinear filtering for automotive systems". In particular, we study how to localize an unknown number of objects, which implies various interesting challenges. We focus on cameras, laser scanners and radar sensors, which are all commonly used in vehicles, and emphasize on situations where we seek to track nearby pedestrians and vehicles. Still, most of the involved methods are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris.

  The course contains a series of videos, quizzes and hands-on assignments where you get to implement several of the most important algorithms.

  Learn from award-winning and passionate teachers to enhanceyour knowledge at the forefront of research on self-driving vehicles. Chalmers is among the top engineering schools that distinguish itself through its close collaboration with industry.

Taught by

Lars Hammarstrand, Yuxuan Xia, Karl Granström and Lennart Svensson

Tags

• sweden