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Hands-On Vision and Behavior for Self-Driving Cars

You're reading from   Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800203587
Length 374 pages
Edition 1st Edition
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Authors (2):
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Krishtof Korda Krishtof Korda
Author Profile Icon Krishtof Korda
Krishtof Korda
Luca Venturi Luca Venturi
Author Profile Icon Luca Venturi
Luca Venturi
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Table of Contents (17) Chapters Close

Preface 1. Section 1: OpenCV and Sensors and Signals
2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER 3. Chapter 2: Understanding and Working with Signals 4. Chapter 3: Lane Detection 5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
6. Chapter 4: Deep Learning with Neural Networks 7. Chapter 5: Deep Learning Workflow 8. Chapter 6: Improving Your Neural Network 9. Chapter 7: Detecting Pedestrians and Traffic Lights 10. Chapter 8: Behavioral Cloning 11. Chapter 9: Semantic Segmentation 12. Section 3: Mapping and Controls
13. Chapter 10: Steering, Throttle, and Brake Control 14. Chapter 11: Mapping Our Environments 15. Assessments 16. Other Books You May Enjoy

Introducing DAVE-2

DAVE-2 is a system designed by Nvidia to train a neural network to drive a car, intended as a proof of concept to demonstrate that, in principle, a single neural network could be able to steer a car on a road. Putting it another way, our network could be trained to drive a real car on a real road, if enough data is provided. To give you an idea, Nvidia used around 72 hours of video, at 10 frames per second.

The idea is very simple: we feed the neural network a video stream, and the neural network will simply generate the steering angle, or something equivalent. The training is created by a human driver, and the system collects data from the camera (training data) and from the steering wheel moved by the pilot (training labels). This is called behavioral cloning because the network is trying to clone the behavior of the human driver.

Unfortunately, this would be a bit too simple, as most of the labels would simply be 0 (the driver going straight), so the network...

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