Self-driving cars are mainly transformational technology that involves cutting-edge robotics, machine learning, and engineering. To become a self-driving car engineer, one must enhance the skills and techniques used by self-driving car teams with the most advanced technologies in the world. The prerequisites of this course are that candidates must learn Python, C++, linear algebra, and calculus. In this course, you will learn the techniques which power self-driving cars across the full stack of autonomous vehicle capabilities. By using deep learning with radar and lidar sensor fusion, one will train the vehicle to detect and identify the environment and surroundings to develop knowledge of navigation.
Interested candidates must enroll for Self Driving Course In Chennai and enhance their skill set. Let us have a look at the detailed requirements:
Computer Vision:
In this course, you will develop knowledge of machine learning skills that are leveraged in autonomous vehicle engineering. Here you will learn about the life cycle of the machine learning project, i.e., framing the problem, choosing the metrics, and improving the models. This course also focuses on the camera sensor and helps learn how to process raw digital images before feeding them with different algorithms. With this course, you will build a convolutional neural network with the help of TensorFlow and also learn to identify objects in images. Here you will be exposed to the whole machine-learning workflow and understand how machine-learning engineer works.
Sensor fusion:
Here you will learn about a key enabler for self-driving cars, i.e., sensor fusion. Self-driving cars rely on other sensors to measure the principles to improve robustness and reliability. Here you will learn about lidar sensors and their roles in the autonomous vehicle sensor suite. This will help you learn to detect objects, such as vehicles, in a 3D lidar point cloud using a deep-learning approach. You will get complete hands-on experience with multi-target tracking and learn how to initialize, update and delete the track, delete tracks and assign measurements for tracking with data association techniques.
Localization:
In this course, you will learn about automatic localization, i.e., from one-dimensional motion, which models up for using 3D point cloud maps, which are obtained from lidar sensors. Here you will move on to using Markov localization for doing 1D object tracking and further leveraging motion models. Here you will learn how to implement two scanning matching algorithms, i.e., iterative closet point and normal distributions transform, etc.
Conclusion:
There are many Autonomous Vehicles Course In Bangalore, and it is essential to choose the right one. This course will teach you how to control a car who have desired trajectory. Also, this course helps you activate the throttle and steering wheel of a car to move it by following a trajectory that is described by coordinates. This course will cover all the basics but also helps with the common controller.
With this course, you will understand the basic principles of feedback control and it will also helps in using autonomous driving techniques.