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In this course, you will :
- Discover the techniques used to power self-driving cars across the entire spectrum of a vehicle's autonomous capabilities.
- develop critical Machine Learning skills that are commonly used in the design of autonomous vehicles
- You will learn about the life cycle of a Machine Learning project, from problem framing and metric selection to training and improving models.
- You will learn how to process raw digital images before feeding them into different algorithms, such as neural networks, if you concentrate on the camera sensor.
- Learn about sensor fusion, a key enabler for self-driving cars.
- Learn how to use a deep-learning approach to detect objects such as vehicles in a 3D lidar point cloud and then evaluate detection performance using a set of cutting-edge metrics.
- Learn everything there is to know about robotic localization, from one-dimensional motion models to using three-dimensional point cloud maps generated by lidar sensors.
- Learn how to implement two scan matching algorithms that work with 2D and 3D data, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP).
- Once you have a desired trajectory, learn how to control a car.
- comprehend the fundamental principles of feedback control and how they are applied in autonomous driving techniques
Syllabus :
- Computer Vision
- Sensor Fusion
- Localization
- Planning
- Control
