Multi-Object Tracking for Automotive Systems

Resumen

Explore the critical role of Multi-Object Tracking (MOT) in the advancement of autonomous vehicles with our comprehensive course. Understand the intricate details of how self-driving cars perceive their environment through an in-depth study of common sensors, motion models, and the principles of filters designed for varying object counts.

Dive into the heart of automotive systems by learning about crucial multi-object tracking filters.

This course is an expansion of previously introduced concepts in "Sensor fusion and nonlinear filtering for automotive systems," focusing on the localization of an unspecified number of objects. We cover essential technologies such as cameras, laser scanners, and radar sensors with a particular emphasis on tracking pedestrians and vehicles in close proximity.

However, the methods taught are versatile, extending their applicability to fields such as surveillance, sports analytics, and space debris tracking.

Engage with our content-rich curriculum featuring videos, quizzes, and practical assignments that offer hands-on experience with key algorithms. With instruction from award-winning educators from Chalmers, a top engineering school known for its industry collaboration, this course on edX is perfect for those looking to enhance their expertise in autonomous vehicles, sensor technologies, and automotive systems.

Categories include Autonomous Vehicles Courses, Sensors Courses, and Automotive Technology Courses, setting you on the path to mastering the dynamic field of automotive systems tracking.

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Impartido por

Lennart Svensson

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Materias