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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Technical requirements

Our lane detection pipeline requires quite a lot of code. We will explain the main concepts, and you can find the full code on GitHub at https://github.com/PacktPublishing/Hands-On-Vision-and-Behavior-for-Self-Driving-Cars/tree/master/Chapter3.

For the instructions and code in this chapter, you need the following:

Python 3.7
The OpenCV-Python module
The NumPy module
The Matplotlib module

To identify the lanes, we need some images and a video. While it's easy to find some open source database to use for this, they are usually only available for non-commercial purposes. For this reason, in this book, we will use images and video generated by two open source projects: CARLA, a simulator useful for autonomous driving tasks, and Speed Dreams, an open source video game. All the techniques also work with real-world footage, and you are encouraged to try them on some public datasets, such as CULane or KITTI.

The Code in Action videos...