Preface xxxv Acknowledgments xxxix How to Read This Book xli Statement on the Use of Generative AI xlv Companion Resources xlvii Acronyms xlix I Foundations of Generative Intelligence for CAVs 1 1 Introduction to Generative AI for CAV Systems 3 1.1 Introduction: The Promise of Generative AI in CAVs 4 1.2 The Paradigm Shift: Generative vs. Discriminative AI 5 1.3 Transformative Capabilities in CAVs 10 1.4 Generative Model Families 13 1.5 The Generative AI Ecosystem 21 1.6 Summary and Roadmap 29 2 Large Language Models: Architecture and Operation 39 2.

1 Introduction 40 2.3 The Rise of Transformers in Language Modeling 47 2.4 The LLM Generation Pipeline 51 2.5 Stage 1: Tokenization and Embeddings 54 2.6 Stage 2: Positional Encoding 57 2.7 Stage 3: Attention and Contextualization 60 2.8 Stage 4: Refinement and Stacking 66 2.9 Stage 5: Output and Sampling 71 2.

10 Model Limitations and Comparative Landscape 74 2.11 Summary 81 II Multimodal Representation and World Grounding 86 3 Diffusion and Flow Matching 87 3.1 Introduction: Why Diffusion for CAV Systems 88 3.2 Conceptual Foundations 90 3.3 Mathematical Foundations 92 3.4 Architectural Choices in Diffusion and Transport Models 101 3.5 Applications in CAV Systems 104 3.6 Limitations and Considerations 107 3.

7 Summary 108 4 GANs, VAEs, and Autoregressive Models 111 4.1 Introduction 112 4.2 Generative Adversarial Networks 114 4.3 Variational Autoencoders 123 4.4 Autoregressive Models for Sequential Prediction 134 4.5 Comparative Analysis 142 4.6 Summary 143 5 Vision-Language Models for Scene Understanding 149 5.1 Introduction 150 5.

2 Architectural Principles of VLMs 151 5.3 Stage I: Visual Feature Extraction 155 5.4 Stage II: Vision-Language Adaptation 160 5.5 Stage III: Integration into the Language Model 164 5.6 Training Methodologies for VLMs 169 5.7 Deployment in Connected and Autonomous Vehicles 178 5.8 Case Study: Semantic Filtering of V2X Messages 181 5.9 Summary 185 6 Video Large Language Models for Temporal Reasoning in Driving Environments 189 6.

1 Introduction 190 6.2 Fundamental Design Principles 191 6.3 Stage I: Spatio-Temporal Encoding 194 6.4 Stage II: Spatio-Temporal Fusion 198 6.5 Stage III: Integration with Language Models 201 6.6 Token Compression Techniques 207 6.7 Representative Architectures 212 6.8 Applications in Connected and Autonomous Vehicles 215 6.

9 Summary 217 III Language-Based Interaction, Memory, and Tool Use 220 7 Prompt Engineering 221 7.1 Introduction and Motivation 222 7.2 Foundations of Prompt Engineering 224 7.3 Design Principles for Effective Prompting 228 7.4 Prompt Engineering in Practice 234 7.5 Prompt Fragility and Safety Considerations in CAV Systems 244 7.6 Summary 245 8 Retrieval-Augmented Generation for Context Grounding 249 8.1 Why RAG Matters in Connected and Autonomous Vehicles 250 8.

2 Architectural Foundations of RAG 254 8.3 RAG vs. Rule-Based Information Systems in CAVs 258 8.4 Mathematical Modeling of RAG 260 8.5 Practical RAG Implementation for CAV Systems 263 8.6 Real-Time Feasibility and System Constraints 267 8.7 Applied Use Cases of RAG in CAV Systems 269 8.8 Summary 272 9 Function Calling and Tool Use 275 9.

1 Introduction: Why CAVs Need Function Calling 276 9.2 System Architecture of Function Calling 279 9.3 Theoretical Foundations of Function Calling 283 9.4 Function Calling in Practice 288 9.5 Function Schema Library and Design Principles 294 9.6 Summary 302 IV Agentic Intelligence and Coordination 306 10 GenAI-Enhanced Agent Architectures 307 10.1 Introduction 308 10.2 Classical Agent Foundations and Architecture 310 10.

3 GenAI Enhancement Concepts 313 10.4 Mathematical Formalization 320 10.5 ReAct Framework 323 10.6 Individual Agent Applications in CAVs 326 10.7 Limitations and Challenges of GenAI-Enhanced Agents 328 10.8 Summary 332 11 GenAI-Enhanced Multi-Agent Systems 335 11.1 Introduction 336 11.2 Multi-Agent Systems Foundations and Architecture 337 11.

3 GenAI Enhancement Concepts 342 11.4 Mathematical Formalization 348 11.5 GenAI-Enhanced Applications in CAVs 354 11.6 Implementation Considerations and Practical Challenges 356 11.7 Summary 361 12 Orchestration Frameworks for Agentic AI 365 12.1 Introduction 366 12.2 Graph-Based Orchestration with LangGraph 368 12.3 Dialogue-Based Orchestration with AutoGen 378 12.

4 Framework Comparison and Selection 387 12.5 Summary 390 V Communication Protocols and Collective Intelligence 392 13 Communication Protocols for Agentic AI 393 13.1 Introduction 394 13.2 Protocol Classification Framework 396 13.3 Protocol Evolution and Development 402 13.4 Protocol Examples and CAV Applications 403 13.5 Summary 406 14 Model Context Protocol for Agentic Systems 409 14.1 Introduction 410 14.

2 Context Provision in CAV Systems 411 14.3 MCP Architecture 415 14.4 Server Features 423 14.5 Client Features 428 14.6 Mathematical Foundations 432 14.7 Practical Implementation 435 14.8 MCP in CAV Systems 439 14.9 Summary 444 15 Agent-to-Agent Protocols for Collaborative Intelligence 447 15.

1 Introduction 448 15.2 A2A Architecture 449 15.3 Mathematical Foundations for Agent Collaboration 458 15.4 Security and Trust 464 15.5 A2A in CAV Systems 466 15.6 Limitations and Challenges 470 15.7 Summary 473 16 Integrated CAV Agent Architectures with MCP and A2A 477 16.1 Introduction 478 16.

2 Protocol Comparison and Decision Framework 479 16.3 System Architecture and Mathematical Framework 482 16.4 Implementation and Execution Patterns 486 16.5 Summary 495 VI Adaptation and Deployment 497 17 Supervised Fine-Tuning 499 17.1 The Necessity of Fine-Tuning in CAV Applications 500 17.2 From Foundation Models to Specialization 502 17.3 Architectural Perspective on Fine-Tuning 504 17.4 Mathematical Formulation of Fine-Tuning 507 17.

5 Practical Fine-Tuning 509 17.6 Parameter-Efficient Fine-Tuning Methods 515 17.7 Use Cases and Deployment Modes 524 17.8 Summary 528 18 Reinforcement Learning Fine-Tuning 531 18.1 Introduction 532 18.2 Mathematical Foundation 536 18.3 Policy Optimization Methods in Detail 544 18.4 RLHF Methodology 559 18.

5 Summary 562 19 Knowledge Distillation for Edge Deployment 567 19.1 Introduction: The Deployment Challenge 568 19.2 The Teacher-Student Paradigm 568 19.3 Core Knowledge Distillation Mechanisms 570 19.4 Advanced Distillation Techniques 577 19.5 Deployment Considerations for CAV Applications 583 19.6 Summary 585 VII Validation and Safety 588 20 Simulation Platforms for GenAI-Driven CAVs 589 20.1 Introduction: Why Simulation Is Indispensable for GenAI-CAVs 590 20.

2 Simulation as a System-Level Substrate for GenAI-CAVs 592 20.3 Scenario Generation and Coverage Expansion 595 20.4 Simulation Platforms for GenAI-CAV Development 597 20.5 Scaling Simulation: From Single-Agent to Multi-Agent Systems 602 20.6 Simulation-Evaluation Boundary 605 20.7 Summary 606 21 Evaluation of Generative AI Systems in CAVs 609 21.1 Introduction: The Role of Evaluation in GenAI-Driven CAVs 610 21.2 Evaluation Dimensions for GenAI Systems in CAV Applications 611 21.

3 Fundamental Evaluation Metrics and Methodologies 615 21.4 Evaluation of Vision-Language Models for CAV Applications 637 21.5 Evaluation of Retrieval-Augmented Generation Systems 641 21.6 Multi-Turn Dialogue and Conversational Agents 648 21.7 Summary 650 22 Ethical and Safety Considerations 655 22.1 Introduction: The Ethical Stakes of Generative AI in CAVs 656 22.2 Foundational Ethical Principles and Challenges 658 22.3 Case Studies: Ethical Challenges in Practice 676 22.

4 Regulatory and Safety Frameworks 679 22.5 Ethical Engineering Methods 683 22.6 Broader Environmental and Societal Considerations 689 22.7 Summary 690 Bibliography 692.