"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 3.