Book Image

Hands-On Vision and Behavior for Self-Driving Cars

By : Luca Venturi, Krishtof Korda
Book Image

Hands-On Vision and Behavior for Self-Driving Cars

By: Luca Venturi, Krishtof Korda

Overview of this book

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.
Table of Contents (17 chapters)
1
Section 1: OpenCV and Sensors and Signals
5
Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
12
Section 3: Mapping and Controls

Chapter 8

  1. DAVE-2, but it can also be called DriveNet.
  2. In a classification task, the image is classified according to some pre-defined categories, while in a regression task we generate a continuous prediction; in our case, for example, a steering angle between –1 and 1.
  3. You can use the yield keyword.
  4. It is a visualization tool that can help us understand where the neural network is focusing its attention.
  5. We need three video streams to help the neural network understand how to correct wrong positions, as the side cameras are effectively corrections from positions far from the center of the car.
  6. For performance reasons, and to be sure that all the code is running only on the client.