LEADER 03968nam 22006495 450 001 9910739467803321 005 20230809092459.0 010 $a981-9928-97-4 024 7 $a10.1007/978-981-99-2897-2 035 $a(MiAaPQ)EBC30683610 035 $a(Au-PeEL)EBL30683610 035 $a(DE-He213)978-981-99-2897-2 035 $a(PPN)272273325 035 $a(CKB)27962356800041 035 $a(EXLCZ)9927962356800041 100 $a20230809d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutonomous driving algorithms and Its IC Design /$fby Jianfeng Ren, Dong Xia 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (306 pages) 311 08$aPrint version: Ren, Jianfeng Autonomous Driving Algorithms and Its IC Design Singapore : Springer Singapore Pte. Limited,c2023 9789819928965 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aWith 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. 606 $aControl engineering 606 $aRobotics 606 $aAutomation 606 $aComputer vision 606 $aComputers 606 $aControl, Robotics, Automation 606 $aComputer Vision 606 $aRobotics 606 $aComputer Hardware 615 0$aControl engineering. 615 0$aRobotics. 615 0$aAutomation. 615 0$aComputer vision. 615 0$aComputers. 615 14$aControl, Robotics, Automation. 615 24$aComputer Vision. 615 24$aRobotics. 615 24$aComputer Hardware. 676 $a629.046 700 $aRen$b Jianfeng$01424379 702 $aXia$b Dong 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739467803321 996 $aAutonomous Driving Algorithms and Its IC Design$93553545 997 $aUNINA