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001475951 001__ 1475951
001475951 003__ OCoLC
001475951 005__ 20231003174625.0
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001475951 008__ 230812s2023\\\\si\\\\\\ob\\\\000\0\eng\d
001475951 019__ $$a1393241358
001475951 020__ $$a9789819928972$$qelectronic book
001475951 020__ $$a9819928974$$qelectronic book
001475951 020__ $$z9819928966
001475951 020__ $$z9789819928965
001475951 0247_ $$a10.1007/978-981-99-2897-2$$2doi
001475951 035__ $$aSP(OCoLC)1393308683
001475951 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP$$dOCLCQ$$dYDX$$dOCLCO
001475951 049__ $$aISEA
001475951 050_4 $$aTL152.8$$b.R46 2023
001475951 08204 $$a629.04/6$$223/eng/20230816
001475951 1001_ $$aRen, Jianfeng,$$eauthor.
001475951 24510 $$aAutonomous driving algorithms and its IC design /$$cJianfeng Ren, Dong Xia.
001475951 264_1 $$aSingapore :$$bSpringer ;$$aBeijing, China :$$bPublishing house of Electronics Industry,$$c[2023]
001475951 300__ $$a1 online resource
001475951 336__ $$atext$$btxt$$2rdacontent
001475951 337__ $$acomputer$$bc$$2rdamedia
001475951 338__ $$aonline resource$$bcr$$2rdacarrier
001475951 500__ $$a4.2.2 Example: Dijkstraś Algorithm for Path Planning
001475951 504__ $$aIncludes bibliographical references.
001475951 5050_ $$aIntro -- Foreword -- Contents -- List of Figures -- List of Tables -- Chapter 1: Challenges of Autonomous Driving Systems -- 1.1 Autonomous Driving -- 1.1.1 Current Autonomous Driving Technology -- 1.2 Autonomous Driving System Challenges -- 1.2.1 Functional Constraints -- 1.2.2 Predictability Constraints -- 1.2.3 Storage Limitations -- 1.2.4 Thermal Constraints -- 1.2.5 Power Is Constrained -- 1.3 Designing an Autonomous Driving System -- 1.3.1 Perception Systems -- 1.3.2 Decision-Making -- 1.3.3 Vehicle Control -- 1.3.4 Safety Verification and Testing
001475951 5058_ $$a1.4 The Autonomous Driving System Computing Platform -- 1.4.1 GPU -- 1.4.2 DSP -- 1.4.3 Field Programmable Gate Array FPGA -- 1.4.4 Specific Integrated Circuit ASIC -- 1.5 The Content of This Book -- 1.5.1 3D Object Detection -- 1.5.2 Lane Detection -- 1.5.3 Motion Planning and Control -- 1.5.4 The Localization and Mapping -- 1.5.5 The Autonomous Driving Simulator -- 1.5.6 Autonomous Driving ASICs -- 1.5.7 Deep Learning Model Optimization -- 1.5.8 Design of Deep Learning Hardware -- 1.5.9 Self-Driving ASICs Design -- 1.5.10 Operating Systems for Autonomous Driving
001475951 5058_ $$a1.5.11 Autonomous Driving Software Architecture -- 1.5.12 5G C-V2X -- References -- Chapter 2: 3D Object Detection -- 2.1 Introduction -- 2.2 Sensors -- 2.2.1 Camera -- 2.2.2 LiDAR -- 2.2.3 Camera + Lidar -- 2.3 Datasets -- 2.4 3D Object Detection Methods -- 2.4.1 Monocular Images Based on Methods -- 2.4.2 Point Cloud-Based Detection Methods -- 2.4.2.1 Projection Methods -- 2.4.2.2 Volumetric Convolution Methods -- 2.4.2.3 Point Net Method -- 2.4.3 Fusion-Based Methods -- 2.5 Complex-YOLO: A Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds [31] -- 2.5.1 Algorithm Overview
001475951 5058_ $$a2.5.2 Point Cloud Preprocessing -- 2.5.3 The Proposed Architecture -- 2.5.4 Anchor Box Design -- 2.5.5 Complex Angle Regression -- 2.5.6 Evaluation on KITTI -- 2.5.7 Training -- 2.5.8 Birdś Eye View Detection -- 2.5.9 3D Object Detection -- 2.6 Future Research Direction -- References -- Chapter 3: Lane Detection -- 3.1 Traditional Image Processing -- 3.2 Example: Lane Detection Based on the Hough Transform -- 3.2.1 Hough Transform -- 3.2.2 Lane Detection -- 3.3 Example: RANSAC Algorithm and Fitting Straight Line -- 3.3.1 Overview of the RANSAC Algorithm
001475951 5058_ $$a3.3.2 Use Python to Implement Line Fitting -- 3.4 Based on Deep Learning -- 3.5 The Multi-Sensor Integration Scheme -- 3.6 Lane Detection Evaluation Criteria -- 3.6.1 Lane Detection System Factors -- 3.6.2 Offline Evaluation -- 3.6.3 Online Evaluation -- 3.6.4 Evaluation Metrics -- 3.7 Example: Lane Detection -- 3.7.1 Overview -- 3.7.2 Loss Function -- 3.7.3 Experimental Results -- 3.7.4 Conclusion -- References -- Chapter 4: Motion Planning and Control -- 4.1 Overview -- 4.2 Traditional Planning and Control Solutions -- 4.2.1 Route Planning
001475951 506__ $$aAccess limited to authorized users.
001475951 520__ $$aWith the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too power-hungry, which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 26 focus on algorithm design for perception and planning control. Chapters 710 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
001475951 588__ $$aDescription based on online resource; title from digital title page (viewed on September 19, 2023).
001475951 650_0 $$aAutomated vehicles.
001475951 650_0 $$aAlgorithms.
001475951 650_0 $$aIntegrated circuits$$xDesign and construction.
001475951 650_6 $$aVéhicules autonomes.
001475951 650_6 $$aAlgorithmes.
001475951 650_6 $$aCircuits intégrés$$xConception et construction.
001475951 655_0 $$aElectronic books.
001475951 7001_ $$aXia, Dong,$$eauthor.
001475951 77608 $$iPrint version:$$aRen, Jianfeng$$tAutonomous Driving Algorithms and Its IC Design$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789819928965
001475951 852__ $$bebk
001475951 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-2897-2$$zOnline Access$$91397441.1
001475951 909CO $$ooai:library.usi.edu:1475951$$pGLOBAL_SET
001475951 980__ $$aBIB
001475951 980__ $$aEBOOK
001475951 982__ $$aEbook
001475951 983__ $$aOnline
001475951 994__ $$a92$$bISE