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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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Questions

After reading this chapter, you will be able to answer the following questions:

What is a distinguished characteristic of DenseNet?
What is the name of the family architecture such as inspired the authors of DenseNet?
What is FC-DenseNet?
Why do we say that FC-DenseNet is U-shaped?
Do you need a fancy architecture like DenseNet to perform semantic segmentation?
If you have a neural network that performs poorly at semantic segmentation, is there a quick fix that you can use sometimes, if you have no other options?
What are skip connections used for in FC-DenseNet and other U-shaped architectures?

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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.

See other products by Venturi

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.

See other products by Korda

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