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ADAS and Automated Driving - Systems Engineering

ADAS and Automated Driving - Systems Engineering (Paperback)

Plato Pathrose (지은이)
SAE International
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ADAS and Automated Driving - Systems Engineering
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· 제목 : ADAS and Automated Driving - Systems Engineering (Paperback) 
· 분류 : 외국도서 > 컴퓨터 > 소프트웨어 개발/엔지니어링 > 시스템 분석/설계
· ISBN : 9781468607444
· 쪽수 : 357쪽
· 출판일 : 2024-03-01

목차

  • "viivii© 2024 SAE International
  • Introduction xiv
  • About This Book xv
  • Assumptions xvi
  • Foreword I xvii
  • Foreword II xix
  • Acknowledgments xxi



    C H A P T E R 1: Introduction to Systems Engineering 11
  • 1.Systems Engineering: An Overview 21
  • 2. Why Do We Need Systems Engineering? 41
  • 3. Evolution of Systems 61
  • 4. ADAS and Automated Driving Systems 81
  • 5. Definition of a System and Its Hierarchy 101
  • 6. System of Systems (SoS) 131
  • 7. Systems Engineering Myths 161
  • 8. Summary 19
  • References 20



    C H A P T E R 2
  • Systems Engineering Life Cycle and Processes 232
  • 1. Systems Engineering Life Cycle 232
  • 1.1. Concept Phase 252
  • 1.2. Design and Development Phase 252
  • 1.3. Production Phase 262
  • 1.4. Operation Phase 262
  • 1.5. Service and Maintenance Phase 272
  • 1.6. Retirement Phase 272
  • 1.7. Re-engineering Phase 27
  • Contents
  • Contentsviii
  • 2.2. System Life Cycle Models 282
  • 2.1. V-Model 282
  • 2.2. Spiral Model 292
  • 2.3. Iterative and Agile Development Model 312
  • 3. Systems Engineering Processes 342
  • 4. Systems Engineering Methods 382
  • 5. Systems Engineering Life Cycle of an Automated Driving System 402
  • 6. Overview of Software-Defined Vehicles and Systems Engineering 422
  • 7. Summary 45
  • References 46

    C H A P T E R 3
  • Agile Methodologies and Systems Engineering 493
  • 1. Introduction to Agile 503
  • 2. Need for Agility in ADAS and Automated Driving 513
  • 3. Agile Methodologies and Their Application 543
  • 3.1. Scrum 553
  • 3.2. Large-Scale Scrum (LeSS) 553
  • 3.3. Scaled Agile Framework® (SAFe®) 563
  • 3.4. Feature-Driven Development (FDD) 573
  • 3.5. Test-Driven Development (TDD) 593
  • 4. Myths about Agile 613
  • 5. Systems Engineering and Agile 633
  • 6. Challenges of Applying Agile in Systems Engineering 683
  • 7. Summary 70
  • References 71

    C H A P T E R 4
  • Concept Phase 734
  • 1. Needs and Requirement Analysis 744
  • 1.1. Concept Generation for Highway Chauffer Function 784
  • 1.2. McCall's Quality Model 80Contents ix4
  • 1.3. Quality Function Deployment 824
  • 1.4. Theory of Inventive Problem-Solving (TRIZ) 844
  • 2. Concept Exploration and Feasibility Analysis 864
  • 2.1. MOE and MOP 864
  • 2.2. Golden Triangle of Concept Generation 874
  • 2.3. How to Generate Concept Alternatives 904
  • 2.4. Operational Concept (OpsCon) 944
  • 3. Concept Definition and Finalization 964
  • 3.1. Concept Selection 964
  • 3.2. Mistakes in Concept Selection 984
  • 3.3. Functional Analysis and Allocation 994
  • 3.4. Concept Validation 1014
  • 4. Summary 103
  • References 103

    C H A P T E R 5
  • System Concept and Modeling 1075
  • 1. System Model 1085
  • 1.1. System Modeling and Its Advantages 1085
  • 1.2. Types of Models 1095
  • 2. System Simulation 1115
  • 2.1. System Model and Simulations 1115
  • 2.2. Operational Simulation 1125
  • 2.3. Physical Simulation 1135
  • 2.4. Environmental Simulation 1135
  • 2.5. Digital Twin and Virtual Reality-Based Simulation 1145
  • 2.6. Hybrid Simulation 1145
  • 2.7. Co-Simulation 1155
  • 2.8. General Requirements for Simulations 1165
  • 3. Modeling System Concepts: A Case Study 1185
  • 4. IDEF: Integrated DEFinition 1245
  • 5. Functional Flow Block Diagrams 1265
  • 6. Trade-Off Analysis, Evaluation, and Decision-Making 1265
  • 7. Summary 131
  • References 132
  • Contentsx

    C H A P T E R 6
  • Predevelopment Phase and Prototyping 1356
  • 1. Bridging Concept Phase and Development Phase 1366
  • 2. Predevelopment of an Automated Driving System: A Case Study 1416
  • 2.1. Preparation Phase 1416
  • 2.2. Development Phase 1426
  • 2.3. Integration Phase 1446
  • 2.4. Testing Phase 1446
  • 3. Measurements in Predevelopment and Prototyping Phase 1476
  • 4. Challenges and Drawbacks in Prototyping 1506
  • 5. Platform Development 1516
  • 6. Summary 153
  • References 154

    C H A P T E R 7
  • System Design and Development Phase 157
  • Part 1: Requirement Analysis, Design, and Architecture 1587
  • 1. Overview of Design and Development Phase 1587
  • 2. Requirement Elicitation and Analysis 1597
  • 3. Functional Analysis and Design 1627
  • 4. System Definition and Component Design 1687
  • 5. System Architecture Definition 1727
  • 6. Characteristics of a Good System Architecture 176
  • Part 2: System Integration, Verification, and Validation 1777
  • 7. System Integration 1777
  • 8. Integration, Calibration, and Tuning of Automated Driving Systems 1837
  • 9. System Verification and Validation 1877
  • 10. Homologation of ADAS and Automated Driving Systems 1917
  • 11. Summary 194
  • References 195
  • Contents xi

    C H A P T E R 8
  • System Production, Operation, and Maintenance Phases 1998
  • 1. Production Phase of an Automated Driving System 2008
  • 1.1. Production Process and FMEA 2028
  • 1.2. Production Systems and Assembly 2048
  • 1.3. Production Preparation 2078
  • 1.4. Calibration of Sensors at the End-of-Line in Production 2098
  • 1.5. Acceptance Testing in the Production and Shipment 2158
  • 2. Importance of Acquiring Production Knowledge 2168
  • 3. System Operation Phase 2178
  • 3.1. System Installation Approaches 2188
  • 3.2. Challenges in the System Operation Phase 2198
  • 4. System Maintenance Phase 2228
  • 4.1. Service and Maintenance of Automated Driving Systems 2248
  • 4.2. Importance of Maintenance History 2268
  • 5. System Upgrade and Re-engineering 2268
  • 6. Summary 228
  • References 228

    C H A P T E R 9
  • Systems Engineering for Artificial Intelligence Components 2319
  • 1. Introduction to AI in Automated Driving Systems 2329
  • 1.1. An Overview of Neural Networks 2349.
  • 2. Data-Driven Software Development in Automated Driving 2369
  • 3. Criteria for Using AI Software Components in a System 2389
  • 4. Requirement Definition and Design of AI Software Components 2409
  • 4.1. Detection Quality Measurements and Requirements for an Object Detection Model 2419
  • 5. Overview of Failure Analysis in Artificial Neural Networks 2449
  • 5.1. Neural Networks Fault Modeling 244
  • 5.2. Overview of Neural Network Faults 2469
  • 5.3. Failures of Object Detection Model at the System Level 248
  • Contentsxii
  • 9.6. Integration, Verification, and Validation of AI Software Components 2489
  • 6.1. Verification and Validation of Functions Utilizing AI Models 2529
  • 6.2. Functional Quality and Performance Evaluation of Object Detection Algorithms 2559
  • 7. AI Software Components in the Operation Phase of a System 2579
  • 7.1. Challenges in Using Self-learning AI Models in Automotive 2589
  • 8. Summary 259
  • References 260

    C H A P T E R 1 0
  • Systems Engineering Management 26310
  • 1. Introduction to Systems Engineering Management (SEM) 26410
  • 1.1. SEM Tasks and Challenges 26510
  • 2. Systems Engineering Management Plan (SEMP) 26610
  • 2.1. Work Breakdown Structure (WBS) 26610
  • 2.2. Structure and Components of a SEMP 26810
  • 3. Risk Management in Systems Engineering 27110
  • 3.1. Risk Assessment and Analysis 27310
  • 3.2. Risk Abatement 27510
  • 4. Systems Engineering and Decision-Making Process 27710
  • 4.1. Strategies for Decision-Making 27810
  • 4.2. Case Study of Selecting a Domain Controllerby Value-Focused Thinking 28010
  • 4.3. Decision Process in Systems Engineering 28310
  • 5. Cost Estimation and Techniques 28610
  • 5.1. Cost Estimation 28610
  • 5.2. Techniques for Cost Estimation 28810
  • 5.3. Cost Breakdown Structure (CBS) in Systems Engineering 28910
  • 6. Summary 290
  • References 292
  • Contents xiii

    C H A P T E R 1 1
  • Methods and Tools for Problem-Solving 29511
  • 1. Introduction to Processes, Methods, and Tools 29611
  • 2. Methods and Techniques for Problem-Solving 29711
  • 2.1. Eight Disciplines of Problem-Solving (8D) 29711
  • 2.2. Root Cause Analysis (RCA) 30311
  • 2.3. Five (5) Whys? 30511
  • 2.4. Fault Tree Analysis (FTA) 30811
  • 2.5. Failure Mode and Effects Analysis (FMEA) 31011
  • 2.6. 3C Methodology 31611
  • 2.7. Plan–Do–Check–Act (PDCA) and Observe–Orient–Decide–Act (OODA) 31711
  • 2.8. Lean Six Sigma Methodology—DMAIC 32011
  • 2.9. Critical Thinking 32111
  • 2.10. Theory of Inventive Problem-Solving (TRIZ) 32311
  • 3. Summary 326
  • References 326

    C H A P T E R 1 2
  • Systems Engineering for Next-Generation Systems 32912
  • 1. Systems: Changes from Past to Present 33012
  • 2. Future Trends in ADAS and Automated Driving Systems 33312
  • 3. Systems Engineering in Software-Defined Vehicles (SDVs) 33712
  • 3.1. Automated Driving System as a Software-Defined System(SDS) 33912
  • 3.2. Role of Software in SDVs 34012
  • 4. Digital Twins in Systems Engineering 34512
  • 5. Skills for the Future 34812
  • 6. Summary 349
  • References 350
  • Index 351
  • About the Author 357

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