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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

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800203587

Length 374 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Authors (2):

Krishtof Korda

Luca Venturi

View More author details

Table of Contents (17) Chapters

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

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Summary

Congratulations, you have completed your quest with your new friends, the langis and the signal! You have had quite an adventure! You battled ghosts and goblins in the form of electromagnetic waves and induced currents. You learned so much along the way about serial versus parallel data transfer; digital versus analog signals; and protocols such as UART, I2C, SPI, CAN, UDP, and TCP and their secret decoder rings! You are now armed with the knowledge you will need when integrating sensors and actuators into your real self-driving car.

In the next chapter, you will learn how to use OpenCV to detect lanes on the road, a vital skill to ensure the safe and legal operation of your self-driving car!

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Authors (2)

Venturi

Venturi

Luca Venturi has extensive experience as a programmer with world-class companies, including Ferrari and Opera Software. He has also worked for some start-ups, including Activetainment (maker of the world's first smart bike), Futurehome (a provider of smart home solutions), and CompanyBook (whose offerings apply artificial intelligence to sales). He worked on the Data Platform team at Tapad (Telenor Group), making petabytes of data accessible to the rest of the company, and is now the lead engineer of Piano Software's analytical database.

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Korda

Korda

Krishtof Korda grew up in a mountainside home over which the US Navy's Blue Angels flew during the Reno Air Races each year. A graduate from the University of Southern California and the USMC Officer Candidate School, he set the Marine Corps obstacle course record of 51 seconds. He took his love of aviation to the USAF, flying aboard the C-5M Super Galaxy as a flight test engineer for 5 years, and engineered installations of airborne experiments for the USAF Test Pilot School for 4 years. Later, he transitioned to designing sensor integrations for autonomous cars at Lyft Level 5. Now he works as an applications engineer for Ouster, integrating LIDAR sensors in the fields of robotics, AVs, drones, and mining, and loves racing Enduro mountain bikes.

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