Creating Autonomous Vehicle Systems

This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map--in addition to training better recognition, tracking, and decision models.

Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled "Teaching and Learning from this Book" was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects.

This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies.

這本書是為一般計算和工程讀者撰寫的自主車輛技術概述之一。作者們分享了他們在設計自主車輛系統方面的實際經驗。這些系統是複雜的，由三個主要子系統組成：(1)用於定位、感知、規劃和控制的演算法；(2)客戶端系統，如機器人操作系統和硬體平台；以及(3)包括數據存儲、模擬、高清地圖和深度學習模型訓練的雲平台。演算法子系統從感測器原始數據中提取有意義的信息，以了解環境並做出未來行動的決策。客戶端子系統整合這些演算法以滿足實時和可靠性要求。雲平台為自主車輛提供離線計算和存儲能力。使用雲平台，可以測試新的演算法以更新高清地圖，並訓練更好的識別、追蹤和決策模型。

自從第一版問世以來，許多大學已將此書納入他們的自主駕駛課程，並且作者們收到了許多讀者的有益評論和反饋。基於此，第二版通過擴展和重寫多個章節以及添加兩個商業測試案例研究進行了改進。此外，還新增了一個名為「從本書中教與學」的新章節，以幫助教師更好地在課堂上利用本書。第二版涵蓋了自主駕駛的最新進展，並提供了可用的實際案例研究，以幫助讀者更好地理解如何在商業自主駕駛項目中應用所學。

這本書對學生、研究人員和從業人員都應該有用。無論您是對自主駕駛感興趣的本科生還是研究生，您都可以在這裡找到整個自主車輛技術堆棧的全面概述。如果您是自主駕駛從業者，本書介紹的許多實用技術將對您有興趣。研究人員還可以找到廣泛的參考資料，以便更深入地探索各種技術。