This is the code repository for Hands-On Vision and Behavior for Self-Driving Cars, published by Packt.
Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4
This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python.
This book covers the following exciting features:
- Understand how to perform camera calibration
- Become well-versed with how lane detection works in self-driving cars using OpenCV
- Explore behavioral cloning by self-driving in a video-game simulator
- Get to grips with using lidars
- Discover how to configure the controls for autonomous vehicles
- Use object detection and semantic segmentation to locate lanes, cars, and pedestrians
- Write a PID controller to control a self-driving car running in a simulator
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
img_threshold = np.zeros_like(channel)
img_threshold [(channel >= 180)] = 255
Following is what you need for this book: This book is for software engineers who are interested in learning about technologies that drive the autonomous car revolution. Although basic knowledge of computer vision and Python programming is required, prior knowledge of advanced deep learning and how to use sensors (lidar) is not needed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 11 | Python 3.7 | Windows, Mac OS X, and Linux (Any) |
| 1 - 11 | TensorFlow 2.2 | Windows, Mac OS X, and Linux (Any) |
| 1 - 11 | Keras 2.3 | Windows, Mac OS X, and Linux (Any) |
| 1 - 11 | CARLA 0.9.9.2 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Click on the following link to see the Code in Action: https://bit.ly/2FeZ5dQ
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.
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.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.