國立高雄大學圖資館 |

Autonomous driving algorithms and its IC design

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Autonomous driving algorithms and its IC designby Jianfeng Ren, Dong Xia.
作者:	Ren, Jianfeng.
其他作者:	Xia, Dong.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xxi, 294 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Automated vehicles.
電子資源:	https://doi.org/10.1007/978-981-99-2897-2
ISBN:	9789819928972$q(electronic bk.)

Autonomous driving algorithms and its IC design
Ren, Jianfeng.

Autonomous driving algorithms and its IC design[electronic resource] /by Jianfeng Ren, Dong Xia. - Singapore :Springer Nature Singapore :2023. - xxi, 294 p. :ill., digital ;24 cm.

Chapter 1 Autonomous Driving: The Challenges -- Chapter 2 3D Object Detection -- Chapter 3 Lane Detection -- Chapter 4 Motion Planning and Control -- Chapter 5 Positioning and Mapping -- Chapter 6 Autonomous Driving Simulator -- Chapter 7 Autonomous Driving Chip -- Chapter 8 Deep Learning Model Optimization -- Chapter 9 Deep Learning Chip Design -- Chapter 10 Autonomous Driving SoC Chip Design -- Chapter 11 Autonomous Driving Operating System -- Chapter 12 Autonomous Driving Software Architecture -- Chapter 13 V2X.

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.

ISBN: 9789819928972$q(electronic bk.)

Standard No.: 10.1007/978-981-99-2897-2doiSubjects--Topical Terms:

849313
Automated vehicles.

LC Class. No.: TL152.8 / .R46 2023

Dewey Class. No.: 629.046

Autonomous driving algorithms and its IC design
LDR:03012nmm a2200325 a 4500 001 647758
003 DE-He213
005 20230809092459.0
006 m d
007 cr nn 008maaau
008 240116s2023 si s 0 eng d
020 $a 9789819928972$q(electronic bk.)
020 $a 9789819928965$q(paper)
024 7 $a 10.1007/978-981-99-2897-2 $2 doi
035 $a 978-981-99-2897-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TL152.8 $b .R46 2023
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 629.046 $2 23
090 $a TL152.8 $b .R393 2023
100 1 $a Ren, Jianfeng. $3 957218
245 1 0 $a Autonomous driving algorithms and its IC design $h [electronic resource] / $c by Jianfeng Ren, Dong Xia.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xxi, 294 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1 Autonomous Driving: The Challenges -- Chapter 2 3D Object Detection -- Chapter 3 Lane Detection -- Chapter 4 Motion Planning and Control -- Chapter 5 Positioning and Mapping -- Chapter 6 Autonomous Driving Simulator -- Chapter 7 Autonomous Driving Chip -- Chapter 8 Deep Learning Model Optimization -- Chapter 9 Deep Learning Chip Design -- Chapter 10 Autonomous Driving SoC Chip Design -- Chapter 11 Autonomous Driving Operating System -- Chapter 12 Autonomous Driving Software Architecture -- Chapter 13 V2X.
520 $a 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.
650 0 $a Automated vehicles. $3 849313
650 0 $a Algorithms. $3 184661
650 0 $a Integrated circuits $x Design and construction. $3 184690
650 1 4 $a Control, Robotics, Automation. $3 914981
650 2 4 $a Computer Vision. $3 913138
650 2 4 $a Robotics. $3 181952
650 2 4 $a Computer Hardware. $3 275482
700 1 $a Xia, Dong. $3 957219
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-2897-2
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)