A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineers
Key Features
- Explore the building blocks of the visual perception system in self-driving cars
- Identify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and Python
- Improve the object detection and classification capabilities of systems with the help of neural networks
Book Description
The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field.
You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You'll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller.
By the end of this book, you'll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers.
What You Will Learn
- 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
Who this book is for
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.
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.
