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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

Self-driving!

Now, you could start running manual_control_drive.py, maybe instructing it to use a lower resolution, using the --res 480x320 parameter.

If you press the D key, the car should start to drive by itself. It's probably quite slow, but it should run, sometimes nicely, sometimes less nicely. It might not always take the turns that it is supposed to take. You can try to add images to the dataset or improve the architecture of the neural network – for example, by adding some dropout layers.

You could try to change the car or increase the speed. You might notice that at a higher speed, the car starts to move more erratically, as if the driver was drunk! This is due to the excessive latency between the car getting in the wrong position and the neural network reacting to it. I think this could be fixed partly with a computer fast enough to process many FPS. However, I think a real fix would be to also record higher speed runs, where the corrections would be stronger...

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