Autonomous Driving Algorithms and Its IC Design
Ren, Jianfeng, Xia, Dong
- 出版商: Springer
- 出版日期: 2024-08-11
- 售價: $2,150
- 貴賓價: 9.5 折 $2,043
- 語言: 英文
- 頁數: 294
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9819928990
- ISBN-13: 9789819928996
-
相關分類:
Algorithms-data-structures
海外代購書籍(需單獨結帳)
相關主題
商品描述
With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too "power-hungry," which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips.
The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2-6 focus on algorithm design for perception and planning control. Chapters 7-10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving.
This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
商品描述(中文翻譯)
隨著人工智慧的快速發展以及各種新型感測器的出現,自動駕駛在近年來變得越來越受歡迎。實現自動駕駛需要新的感測數據來源,例如攝影機、雷達和激光雷達,而算法處理則需要高度的並行計算能力。在這方面,傳統的CPU計算能力不足,而DSP在影像處理方面表現良好,但在深度學習的性能上則不夠充分。雖然GPU在訓練方面表現優異,但其過於「耗電」的特性可能會影響車輛性能。因此,本書展望未來,主張定制ASIC必將成為主流。本書以自動駕駛的IC設計為目標,討論設計未來導向的自動駕駛SoC晶片的理論與工程實踐。
內容分為十三章,第一章主要介紹自動駕駛目前面臨的挑戰和研究方向。第二至第六章專注於感知和規劃控制的算法設計。第七至第十章則探討深度學習模型的優化及深度學習晶片的設計,而第十一至第十二章涵蓋自動駕駛軟體架構設計。第十三章討論5G在自動駕駛中的應用。
本書適合所有對自動駕駛感興趣的本科生、研究生及工程技術人員。
作者簡介
Dr. Jianfeng Ren, currently working at Google Inc., received his PhD in Electrical Engineering from the University of Texas at Dallas in 2009. Previously, he worked for Qualcomm and Huawei HiSilicon for many years, and has published more than 40 papers and more than 30 US patents. His current research focus is on computational photograph/computer vision algorithms.
Dr. Dong xia, received the PhD degree in Communication and Information System in the National University of Defense Technology. He has long been engaged in research work in the field of artificial intelligence, chip algorithm design and automatic target recognition, and published more than 60 patents.
作者簡介(中文翻譯)
Dr. Jianfeng Ren,目前在 Google Inc. 工作,於 2009 年在德克薩斯大學達拉斯分校獲得電機工程博士學位。之前,他在 Qualcomm 和華為海思工作多年,並發表了超過 40 篇論文和 30 多項美國專利。他目前的研究重點是計算攝影/計算機視覺算法。
Dr. Dong Xia,於國防科技大學獲得通信與信息系統博士學位。他長期從事人工智慧、晶片算法設計和自動目標識別領域的研究工作,並發表了超過 60 項專利。