This module will run through the principles of sensor fusion, their architecture, algorithms and automotive applications. Sensor fusion is one of the most important topics in the field of autonomous vehicles and you will understand how fusion algorithms can be created to improve vehicle perception for safe driving.
The module goals are
- To gain an understanding of principles of fusing rich sensor data produced by LiDAR, Camera, Radar to better observe and analyse the scene around a moving vehicle
- To understand how these principles are applied in real-life applications, for inference purposes and to enhance sensor outputs
- To obtain an overview of the current state of the art in the area of road user detection and tracking and trends in current and future research
Module Content
Introduction and rationale for sensor fusion [20:36]
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Sensor fusion strategies [18:14]
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Theoretical foundations [21:53]
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Likelihood estimation for rich sensors [21:53]
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Rich sensor fusion for road user detection [24:45]
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Temporal fusion [19:59]
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Pixel level fusion [26:12]
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Practical fusion methods [25:43]
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Homework Assignment
Emerging topics, outlook, and conclusions [22:35]
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Additional Material
Assignment details
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Further exploration of material
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Additional reading
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Sensor fusion for safe driving
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