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 9

  1. The dense blocks, where each layer is connected to all the output of the previous layers, including the input.
  2. ResNet.
  3. It is an adaptation of DenseNet for performing semantic segmentation.
  4. Because it can be visualized like a U, with the left side downsampling and the right side upsampling.
  5. No, you can just stack a series of convolutions. However, due to the high resolution, achieving a good result will be challenging, and most likely would use a lot of memory and be quite slow.
  6. You can use a median blur to remove the salt and pepper noise that can be present in the segmentation masks of poorly trained networks.
  7. They are used to propagate high-resolution channels and help the network achieve a good real resolution.