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 6

  1. To increase the number of non-linear activations and let the network learn more complex functions.
  2. Not necessarily. In fact, a well-crafted deep network can be faster and more precise.
  3. When the training accuracy increases but the validation accuracy decreases.
  4. Early stopping.
  5. With batch normalization.
  6. Using data augmentation.
  7. Because it learns not to rely on only a few channels.
  8. The training will probably be slower.
  9. The training will probably be slower.