Hands-On Vision and Behavior for Self-Driving Cars: Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

Venturi, Luca, Korda, Krishtof

商品描述

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.

商品描述(中文翻譯)

一本針對電腦視覺和自主系統工程師學習自駕車視覺感知的實用指南

主要特點:
- 探索自駕車視覺感知系統的基本組件
- 使用開源工具如OpenCV和Python識別物體和車道,定義行駛表面的邊界
- 借助神經網絡提升系統的物體檢測和分類能力

書籍描述:
自駕車的視覺感知能力是由電腦視覺技術提供支持的。與自駕車相關的工作可以大致分為三個組件:機器人技術、電腦視覺和機器學習。本書為現有的電腦視覺工程師和開發人員提供了參與這一蓬勃發展領域的獨特機會。

您將學習有關應用於無人駕駛汽車的電腦視覺、深度學習和深度感知的知識。本書提供了結構化和全面的介紹,因為製作一輛真正的自駕車需要跨職能的巨大努力。隨著學習的進展,您將使用實際代碼處理相關案例,然後了解如何使用OpenCV、TensorFlow和Keras分析來自汽車攝像頭的視頻流。之後,您將學習如何解釋和充分利用光達(光探測和測距)來識別障礙物並定位自己的位置。您甚至可以應對自駕車中的核心挑戰,如尋找車道、檢測行人和交通燈、執行語義分割以及編寫PID控制器。

通過閱讀本書,您將具備為在無人駕駛汽車模擬器中運行的自駕車編寫代碼所需的技能,並能應對自主車輛工程師面臨的各種挑戰。

您將學到:
- 瞭解如何進行攝像頭校準
- 熟悉使用OpenCV在自駕車中進行車道檢測的工作原理
- 通過在視頻遊戲模擬器中自駕車進行行為克隆來探索
- 掌握使用光達的技巧
- 發現如何配置自主車輛的控制
- 使用物體檢測和語義分割來定位車道、車輛和行人
- 編寫PID控制器來控制在模擬器中運行的自駕車

本書適合對推動自駕車革命的技術感興趣的軟體工程師閱讀。雖然需要基本的電腦視覺和Python編程知識,但不需要事先了解深度學習和如何使用傳感器(光達)。