About This Course: Multi-Object Tracking for Automotive Systems

Course Description

This advanced course dives into the fundamental techniques and theories behind multi-object tracking (MOT) in the realm of autonomous vehicles, specifically focusing on environmental perception through various sensors like cameras, laser scanners, and radars. With a structured combination of videos, quizzes, and hands-on assignments, students will not only gain theoretical insight but also practical experience in implementing significant MOT algorithms. Offered by ChalmersX, an institution renowned for its strong academia-industry nexus, this course benefits from leading-edge research tailored for real-world applications.

What Students Will Learn

• A thorough understanding of multi-object tracking (MOT) and its challenges.
• Expert-level knowledge of the principles, theory, and algorithms in modern MOT.
• Extensive know-how for solving various MOT problems practically.
• Practical experience from implementing different MOT algorithms.

Prerequisites

Students are expected to have completed the course CHM013x: Sensor Fusion and Non-linear Filtering for Automotive Systems, or have equivalent knowledge in the field of sensor technology and nonlinear filtering applied to automotive technology.

Course Coverage

• Fundamentals of the common sensors and motion models used in autonomous vehicles.
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• Principles and operations of filters to manage data from varying number of objects.
• Comprehensive study on multiple-object tracking (MOT) filters and their applications.
• Practical scenarios focusing on tracking pedestrians and vehicles using multiple sensor modalities.

Who This Course Is For

This course is suited for students, engineers, and professionals in the fields of automotive engineering, robotics, or any related field seeking advanced knowledge and skills in multi-object tracking and sensor fusion technology.

Application of Learned Skills in Real World

Skills acquired from this course can be applied in various fields, not limited to automotive systems. These include surveillance, sports analytics, space missions, and biological research, where precise and dynamic tracking of multiple objects is crucial. Moreover, a deeper understanding of MOT can lead to advancements in autonomous vehicle technology, making transportation safer and more efficient.