Preface

Self-driving cars will soon be among us. The improvements seen in this field have been nothing short of extraordinary. The first time I heard about self-driving cars, it was in 2010, when I tried one in the Toyota showroom in Tokyo. The ride cost around a dollar. The car was going very slowly, and it was apparently dependent on sensors embedded in the road.

Fast forward a few years, lidar and advancements in computer vision and deep learning have made that technology look primitive and unnecessarily invasive and expensive.

In the course of this book, we will use OpenCV for a variety of tasks, including pedestrian detection and lane detection; you will discover deep learning and learn how to leverage it for image classification, object detection, and semantic segmentation, using it to identify pedestrians, cars, roads, sidewalks, and crossing lights, while learning about some of the most influential neural networks.

You will get comfortable using the CARLA simulator, which you will use to control a car using behavioral cloning and a PID controller; you will learn about network protocols, sensors, cameras, and how to use lidar to map the world around you and to find your position.

But before diving into these amazing technologies, please take a moment and try to imagine the future in 20 years. What are the cars like? They can drive by themselves. But can they also fly? Are there still crossing lights? How fast, heavy, and expensive are those cars? How do we use them, and how often? What about self-driving buses and trucks?

We cannot know the future, but it is conceivable that self-driving cars, and self-driving things in general, will shape our daily lives and our cities in new and exciting ways.

Do you want to play an active role in defining this future? If so, keep reading. This book can be the first step of your journey.