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Cyber-Physical-Human Systems. Fundamentals and Applications. Edition No. 1. IEEE Press Series on Technology Management, Innovation, and Leadership

  • Book

  • 592 Pages
  • June 2023
  • John Wiley and Sons Ltd
  • ID: 5837991
Cyber-Physical-Human Systems

A comprehensive edited volume exploring the latest in the interactions between cyber-physical systems and humans

In Cyber-Physical-Human Systems: Fundamentals and Applications, a team of distinguished researchers delivers a robust and up-to-date volume of contributions from leading researchers on Cyber-Physical-Human Systems, an emerging class of systems with increased interactions between cyber-physical, and human systems communicating with each other at various levels across space and time, so as to achieve desired performance related to human welfare, efficiency, and sustainability.

The editors have focused on papers that address the power of emerging CPHS disciplines, all of which feature humans as an active component during cyber and physical interactions. Articles that span fundamental concepts and methods to various applications in engineering sectors of transportation, robotics, and healthcare and general socio-technical systems such as smart cities are featured. Together, these articles address challenges and opportunities that arise due to the emerging interactions between cyber-physical systems and humans, allowing readers to appreciate the intersection of cyber-physical system research and human behavior in large-scale systems.

In the book, readers will also find: - A thorough introduction to the fundamentals of cyber-physical-human systems - In-depth discussions of cyber-physical-human systems with applications in transportation, robotics, and healthcare - A comprehensive treatment of socio-technical systems, including social networks and smart cities

Perfect for cyber-physical systems researchers, academics, and graduate students, Cyber-Physical-Human Systems: Fundamentals and Applications will also earn a place in the libraries of research and development professionals working in industry and government agencies.

Table of Contents

A Note from the Series Editor xvii

About the Editors xviii

List of Contributors xix

Introduction xxvii

Part I Fundamental Concepts and Methods 1

1 Human-in-the-Loop Control and Cyber-Physical-Human Systems: Applications and Categorization 3
Tariq Samad

1.1 Introduction 3

1.2 Cyber + Physical + Human 4

1.2.1 Cyberphysical Systems 5

1.2.2 Physical-Human Systems 6

1.2.3 Cyber-Human Systems 6

1.3 Categorizing Human-in-the-Loop Control Systems 6

1.3.1 Human-in-the-Plant 8

1.3.2 Human-in-the-Controller 8

1.3.3 Human-Machine Control Symbiosis 10

1.3.4 Humans-in-Multiagent-Loops 11

1.4 A Roadmap for Human-in-the-Loop Control 13

1.4.1 Self- and Human-Driven Cars on Urban Roads 13

1.4.2 Climate Change Mitigation and Smart Grids 14

1.5 Discussion 15

1.5.1 Other Ways of Classifying Human-in-the-Loop Control 15

1.5.2 Modeling Human Understanding and Decision-Making 16

1.5.3 Ethics and CPHS 18

1.6 Conclusions 19

Acknowledgments 19

References 20

2 Human Behavioral Models Using Utility Theory and Prospect Theory 25
Anuradha M. Annaswamy and Vineet Jagadeesan Nair

2.1 Introduction 25

2.2 Utility Theory 26

2.2.1 An Example 27

2.3 Prospect Theory 27

2.3.1 An Example: CPT Modeling for SRS 30

2.3.1.1 Detection of CPT Effects via Lotteries 32

2.3.2 Theoretical Implications of CPT 33

2.3.2.1 Implication I: Fourfold Pattern of Risk Attitudes 34

2.3.2.2 Implication II: Strong Risk Aversion Over Mixed Prospects 36

2.3.2.3 Implication III: Effects of Self-Reference 37

2.4 Summary and Conclusions 38

Acknowledgments 39

References 39

3 Social Diffusion Dynamics in Cyber-Physical-Human Systems 43
Lorenzo Zino and Ming Cao

3.1 Introduction 43

3.2 General Formalism for Social Diffusion in CPHS 45

3.2.1 Complex and Multiplex Networks 45

3.2.2 General Framework for Social Diffusion 46

3.2.3 Main Theoretical Approaches 48

3.3 Modeling Decision-Making 49

3.3.1 Pairwise Interaction Models 49

3.3.2 Linear Threshold Models 52

3.3.3 Game-Theoretic Models 53

3.4 Dynamics in CPHS 55

3.4.1 Social Diffusion in Multiplex Networks 56

3.4.2 Co-Evolutionary Social Dynamics 58

3.5 Ongoing Efforts Toward Controlling Social Diffusion and Future Challenges 62

Acknowledgments 63

References 63

4 Opportunities and Threats of Interactions Between Humans and Cyber-Physical Systems - Integration and Inclusion Approaches for Cphs 71
Frédéric Vanderhaegen and Victor Díaz Benito Jiménez

4.1 CPHS and Shared Control 72

4.2 “Tailor-made” Principles for Human-CPS Integration 73

4.3 “All-in-one” based Principles for Human-CPS Inclusion 74

4.4 Dissonances, Opportunities, and Threats in a CPHS 76

4.5 Examples of Opportunities and Threats 79

4.6 Conclusions 85

References 86

5 Enabling Human-Aware Autonomy Through Cognitive Modeling and Feedback Control 91
Neera Jain, Tahira Reid, Kumar Akash, Madeleine Yuh, and Jacob Hunter

5.1 Introduction 91

5.1.1 Important Cognitive Factors in HAI 92

5.1.2 Challenges with Existing CPHS Methods 93

5.1.3 How to Read This Chapter 95

5.2 Cognitive Modeling 95

5.2.1 Modeling Considerations 95

5.2.2 Cognitive Architectures 97

5.2.3 Computational Cognitive Models 98

5.2.3.1 ARMAV and Deterministic Linear Models 99

5.2.3.2 Dynamic Bayesian Models 99

5.2.3.3 Decision Analytical Models 100

5.2.3.4 POMDP Models 102

5.3 Study Design and Data Collection 103

5.3.1 Frame Research Questions and Identify Variables 104

5.3.2 Formulate Hypotheses or Determine the Data Needed 105

5.3.2.1 Hypothesis Testing Approach 105

5.3.2.2 Model Training Approach 105

5.3.3 Design Experiment and/or Study Scenario 107

5.3.3.1 Hypothesis Testing Approach 107

5.3.3.2 Model Training Approach 107

5.3.4 Conduct Pilot Studies and Get Initial Feedback; Do Preliminary Analysis 108

5.3.5 A Note about Institutional Review Boards and Recruiting Participants 109

5.4 Cognitive Feedback Control 109

5.4.1 Considerations for Feedback Control 110

5.4.2 Approaches 111

5.4.2.1 Heuristics-Based Planning 111

5.4.2.2 Measurement-Based Feedback 112

5.4.2.3 Goal-Oriented Feedback 112

5.4.2.4 Case Study 112

5.4.3 Evaluation Methods 113

5.5 Summary and Opportunities for Further Investigation 113

5.5.1 Model Generalizability and Adaptability 114

5.5.2 Measurement of Cognitive States 114

5.5.3 Human Subject Study Design 114

References 115

6 Shared Control with Human Trust and Workload Models 125
Murat Cubuktepe, Nils Jansen, and Ufuk Topcu

6.1 Introduction 125

6.1.1 Review of Shared Control Methods 126

6.1.2 Contribution and Approach 127

6.1.3 Review of IRL Methods Under Partial Information 128

6.1.3.1 Organization 129

6.2 Preliminaries 129

6.2.1 Markov Decision Processes 129

6.2.2 Partially Observable Markov Decision Processes 130

6.2.3 Specifications 130

6.3 Conceptual Description of Shared Control 131

6.4 Synthesis of the Autonomy Protocol 132

6.4.1 Strategy Blending 132

6.4.2 Solution to the Shared Control Synthesis Problem 133

6.4.2.1 Nonlinear Programming Formulation for POMDPs 133

6.4.2.2 Strategy Repair Using Sequential Convex Programming 134

6.4.3 Sequential Convex Programming Formulation 135

6.4.4 Linearizing Nonconvex Problem 135

6.4.4.1 Linearizing Nonconvex Constraints and Adding Slack Variables 135

6.4.4.2 Trust Region Constraints 136

6.4.4.3 Complete Algorithm 136

6.4.4.4 Additional Specifications 136

6.4.4.5 Additional Measures 137

6.5 Numerical Examples 137

6.5.1 Modeling Robot Dynamics as POMDPs 138

6.5.2 Generating Human Demonstrations 138

6.5.3 Learning a Human Strategy 139

6.5.4 Task Specification 139

6.5.5 Results 140

6.6 Conclusion 140

Acknowledgments 140

References 140

7 Parallel Intelligence for CPHS: An ACP Approach 145
Xiao Wang, Jing Yang, Xiaoshuang Li, and Fei-Yue Wang

7.1 Background and Motivation 145

7.2 Early Development in China 147

7.3 Key Elements and Framework 149

7.4 Operation and Process 151

7.4.1 Construction of Artificial Systems 152

7.4.2 Computational Experiments in Parallel Intelligent Systems 152

7.4.3 Closed-Loop Optimization Based on Parallel Execution 153

7.5 Applications 153

7.5.1 Parallel Control and Intelligent Control 154

7.5.2 Parallel Robotics and Parallel Manufacturing 156

7.5.3 Parallel Management and Intelligent Organizations 157

7.5.4 Parallel Medicine and Smart Healthcare 158

7.5.5 Parallel Ecology and Parallel Societies 160

7.5.6 Parallel Economic Systems and Social Computing 161

7.5.7 Parallel Military Systems 163

7.5.8 Parallel Cognition and Parallel Philosophy 164

7.6 Conclusion and Prospect 165

References 165

Part II Transportation 171

8 Regularities of Human Operator Behavior and Its Modeling 173
Aleksandr V. Efremov

8.1 Introduction 173

8.2 The Key Variables in Man-Machine Systems 174

8.3 Human Responses 177

8.4 Regularities of Man-Machine System in Manual Control 180

8.4.1 Man-Machine System in Single-loop Compensatory System 180

8.4.2 Man-Machine System in Multiloop, Multichannel, and Multimodal Tasks 185

8.4.2.1 Man-Machine System in the Multiloop Tracking Task 185

8.4.2.2 Man-Machine System in the Multichannel Tracking Task 187

8.4.2.3 Man-Machine System in Multimodal Tracking Tasks 188

8.4.2.4 Human Operator Behavior in Pursuit and Preview Tracking Tasks 191

8.5 Mathematical Modeling of Human Operator Behavior in Manual Control Task 194

8.5.1 McRuer’s Model for the Pilot Describing Function 194

8.5.1.1 Single-Loop Compensatory Model 194

8.5.1.2 Multiloop and Multimodal Compensatory Model 197

8.5.2 Structural Human Operator Model 197

8.5.3 Pilot Optimal Control Model 199

8.5.4 Pilot Models in Preview and Pursuit Tracking Tasks 201

8.6 Applications of the Man-Machine System Approach 202

8.6.1 Development of Criteria for Flying Qualities and PIO Prediction 203

8.6.1.1 Criteria of FQ and PIO Prediction as a Requirement for the Parameters of the Pilot-Aircraft System 203

8.6.1.2 Calculated Piloting Rating of FQ as the Criteria 205

8.6.2 Interfaces Design 206

8.6.3 Optimization of Control System and Vehicle Dynamics Parameters 210

8.7 Future Research Challenges and Visions 213

8.8 Conclusion 214

References 215

9 Safe Shared Control Between Pilots and Autopilots in the Face of Anomalies 219
Emre Eraslan, Yildiray Yildiz, and Anuradha M. Annaswamy

9.1 Introduction 219

9.2 Shared Control Architectures: A Taxonomy 221

9.3 Recent Research Results 222

9.3.1 Autopilot 224

9.3.1.1 Dynamic Model of the Aircraft 224

9.3.1.2 Advanced Autopilot Based on Adaptive Control 225

9.3.1.3 Autopilot Based on Proportional Derivative Control 228

9.3.2 Human Pilot 228

9.3.2.1 Pilot Models in the Absence of Anomaly 228

9.3.2.2 Pilot Models in the Presence of Anomaly 229

9.3.3 Shared Control 230

9.3.3.1 SCA1: A Pilot with a CfM-Based Perception and a Fixed-Gain Autopilot 231

9.3.3.2 SCA2: A Pilot with a CfM-Based Decision-Making and an Advanced Adaptive Autopilot 232

9.3.4 Validation with Human-in-the-Loop Simulations 232

9.3.5 Validation of Shared Control Architecture 1 234

9.3.5.1 Experimental Setup 234

9.3.5.2 Anomaly 235

9.3.5.3 Experimental Procedure 235

9.3.5.4 Details of the Human Subjects 236

9.3.5.5 Pilot-Model Parameters 237

9.3.5.6 Results and Observations 237

9.3.6 Validation of Shared Control Architecture 2 240

9.3.6.1 Experimental Setup 241

9.3.6.2 Anomaly 241

9.3.6.3 Experimental Procedure 242

9.3.6.4 Details of the Human Subjects 243

9.3.6.5 Results and Observations 244

9.4 Summary and Future Work 246

References 247

10 Safe Teleoperation of Connected and Automated Vehicles 251
Frank J. Jiang, Jonas Mårtensson, and Karl H. Johansson

10.1 Introduction 251

10.2 Safe Teleoperation 254

10.2.1 The Advent of 5G 258

10.3 CPHS Design Challenges in Safe Teleoperation 259

10.4 Recent Research Advances 261

10.4.1 Enhancing Operator Perception 261

10.4.2 Safe Shared Autonomy 264

10.5 Future Research Challenges 267

10.5.1 Full Utilization of V2X Networks 267

10.5.2 Mixed Autonomy Traffic Modeling 268

10.5.3 5G Experimentation 268

10.6 Conclusions 269

References 270

11 Charging Behavior of Electric Vehicles 273
Qing-Shan Jia and Teng Long

11.1 History, Challenges, and Opportunities 274

11.1.1 The History and Status Quo of EVs 274

11.1.2 The Current Challenge 276

11.1.3 The Opportunities 277

11.2 Data Sets and Problem Modeling 278

11.2.1 Data Sets of EV Charging Behavior 278

11.2.1.1 Trend Data Sets 279

11.2.1.2 Driving Data Sets 279

11.2.1.3 Battery Data Sets 279

11.2.1.4 Charging Data Sets 279

11.2.2 Problem Modeling 281

11.3 Control and Optimization Methods 284

11.3.1 The Difficulty of the Control and Optimization 284

11.3.2 Charging Location Selection and Routing Optimization 285

11.3.3 Charging Process Control 286

11.3.4 Control and Optimization Framework 287

11.3.4.1 Centralized Optimization 287

11.3.4.2 Decentralized Optimization 288

11.3.4.3 Hierarchical Optimization 288

11.3.5 The Impact of Human Behaviors 289

11.4 Conclusion and Discussion 289

References 290

Part III Robotics 299

12 Trust-Triggered Robot-Human Handovers Using Kinematic Redundancy for Collaborative Assembly in Flexible Manufacturing 301
S. M. Mizanoor Rahman, Behzad Sadrfaridpour, Ian D. Walker, and Yue Wang

12.1 Introduction 301

12.2 The Task Context and the Handover 303

12.3 The Underlying Trust Model 304

12.4 Trust-Based Handover Motion Planning Algorithm 305

12.4.1 The Overall Motion Planning Strategy 305

12.4.2 Manipulator Kinematics and Kinetics Models 305

12.4.3 Dynamic Impact Ellipsoid 306

12.4.4 The Novel Motion Control Approach 307

12.4.5 Illustration of the Novel Algorithm 308

12.5 Development of the Experimental Settings 310

12.5.1 Experimental Setup 310

12.5.1.1 Type I: Center Console Assembly 310

12.5.1.2 Type II: Hose Assembly 311

12.5.2 Real-Time Measurement and Display of Trust 311

12.5.2.1 Type I: Center Console Assembly 311

12.5.2.2 Type II: Hose Assembly 313

12.5.2.3 Trust Computation 313

12.5.3 Plans to Execute the Trust-Triggered Handover Strategy 314

12.5.3.1 Type I Assembly 314

12.5.3.2 Type II Assembly 314

12.6 Evaluation of the Motion Planning Algorithm 315

12.6.1 Objective 315

12.6.2 Experiment Design 315

12.6.3 Evaluation Scheme 315

12.6.4 Subjects 316

12.6.5 Experimental Procedures 316

12.6.5.1 Type I Assembly 317

12.6.5.2 Type II Assembly 317

12.7 Results and Analyses, Type I Assembly 318

12.8 Results and Analyses, Type II Assembly 322

12.9 Conclusions and Future Work 323

Acknowledgment 324

References 324

13 Fusing Electrical Stimulation and Wearable Robots with Humans to Restore and Enhance Mobility 329
Thomas Schauer, Eduard Fosch-Villaronga, and Juan C. Moreno

13.1 Introduction 329

13.1.1 Functional Electrical Stimulation 330

13.1.2 Spinal Cord Stimulation 331

13.1.3 Wearable Robotics (WR) 332

13.1.4 Fusing FES/SCS and Wearable Robotics 334

13.2 Control Challenges 335

13.2.1 Feedback Approaches to Promote Volition 336

13.2.2 Principles of Assist-as-Needed 336

13.2.3 Tracking Control Problem Formulation 336

13.2.4 Co-operative Control Strategies 337

13.2.5 EMG- and MMG-Based Assessment of Muscle Activation 344

13.3 Examples 345

13.3.1 A Hybrid Robotic System for Arm Training of Stroke Survivors 345

13.3.2 First Certified Hybrid Robotic Exoskeleton for Gait Rehabilitation Settings 347

13.3.3 Body Weight-Supported Robotic Gait Training with tSCS 348

13.3.4 Modular FES and Wearable Robots to Customize Hybrid Solutions 348

13.4 Transfer into Daily Practice: Integrating Ethical, Legal, and Societal Aspects into the Design 350

13.5 Summary and Outlook 352

Acknowledgments 353

Acronyms 353

References 354

14 Contemporary Issues and Advances in Human-Robot Collaborations 365
Takeshi Hatanaka, Junya Yamauchi, Masayuki Fujita, and Hiroyuki Handa

14.1 Overview of Human-Robot Collaborations 365

14.1.1 Task Architecture 366

14.1.2 Human-Robot Team Formation 368

14.1.3 Human Modeling: Control and Decision 369

14.1.4 Human Modeling: Other Human Factors 371

14.1.5 Industrial Perspective 372

14.1.6 What Is in This Chapter 375

14.2 Passivity-Based Human-Enabled Multirobot Navigation 376

14.2.1 Architecture Design 377

14.2.2 Human Passivity Analysis 379

14.2.3 Human Workload Analysis 381

14.3 Operation Support with Variable Autonomy via Gaussian Process 383

14.3.1 Design of the Operation Support System with Variable Autonomy 385

14.3.2 User Study 388

14.3.2.1 Operational Verification 388

14.3.2.2 Usability Test 390

14.4 Summary 391

Acknowledgments 393

References 393

Part IV Healthcare 401

15 Overview and Perspectives on the Assessment and Mitigation of Cognitive Fatigue in Operational Settings 403
Mike Salomone, Michel Audiffren, and Bruno Berberian

15.1 Introduction 403

15.2 Cognitive Fatigue 404

15.2.1 Definition 404

15.2.2 Origin of Cognitive Fatigue 404

15.2.3 Effects on Adaptive Capacities 406

15.3 Cyber-Physical System and Cognitive Fatigue: More Automation Does Not Imply Less Cognitive Fatigue 406

15.4 Assessing Cognitive Fatigue 409

15.4.1 Subjective Measures 409

15.4.2 Behavioral Measures 410

15.4.3 Physiological Measurements 410

15.5 Limitations and Benefits of These Measures 412

15.6 Current and Future Solutions and Countermeasures 412

15.6.1 Physiological Computing: Toward Real-Time Detection and Adaptation 412

15.7 System Design and Explainability 414

15.8 Future Challenges 415

15.8.1 Generalizing the Results Observed in the Laboratory to Ecological Situations 415

15.8.2 Determining the Specificity of Cognitive Fatigue 415

15.8.3 Recovering from Cognitive Fatigue 417

15.9 Conclusion 418

References 419

16 Epidemics Spread Over Networks: Influence of Infrastructure and Opinions 429
Baike She, Sebin Gracy, Shreyas Sundaram, Henrik Sandberg, Karl H. Johansson, andPhilipE.Paré

16.1 Introduction 429

16.1.1 Infectious Diseases 429

16.1.2 Modeling Epidemic Spreading Processes 430

16.1.3 Susceptible-Infected-Susceptible (SIS) Compartmental Models 431

16.2 Epidemics on Networks 432

16.2.1 Motivation 432

16.2.2 Modeling Epidemics over Networks 433

16.2.3 Networked Susceptible-Infected-Susceptible Epidemic Models 434

16.3 Epidemics and Cyber-Physical-Human Systems 436

16.3.1 Epidemic and Opinion Spreading Processes 437

16.3.2 Epidemic and Infrastructure 438

16.4 Recent Research Advances 439

16.4.1 Notation 439

16.4.2 Epidemic and Opinion Spreading Processes 440

16.4.2.1 Opinions Over Networks with Both Cooperative and Antagonistic Interactions 440

16.4.2.2 Coupled Epidemic and Opinion Dynamics 441

16.4.2.3 Opinion-Dependent Reproduction Number 443

16.4.2.4 Simulations 444

16.4.3 Epidemic Spreading with Shared Resources 445

16.4.3.1 The Multi-Virus SIWS Model 445

16.4.3.2 Problem Statements 447

16.4.3.3 Analysis of the Eradicated State of a Virus 448

16.4.3.4 Persistence of a Virus 449

16.4.3.5 Simulations 449

16.5 Future Research Challenges and Visions 450

References 451

17 Digital Twins and Automation of Care in the Intensive Care Unit 457
J. Geoffrey Chase, Cong Zhou, Jennifer L. Knopp, Knut Moeller, Balázs Benyo, Thomas Desaive, Jennifer H. K. Wong, Sanna Malinen, Katharina Naswall, Geoffrey M. Shaw, Bernard Lambermont, and Yeong S. Chiew

17.1 Introduction 457

17.1.1 Economic Context 458

17.1.2 Healthcare Context 459

17.1.3 Technology Context 460

17.1.4 Overall Problem and Need 460

17.2 Digital Twins and CPHS 461

17.2.1 Digital Twin/Virtual Patient Definition 461

17.2.2 Requirements in an ICU Context 463

17.2.3 Digital Twin Models in Key Areas of ICU Care and Relative to Requirements 464

17.2.4 Review of Digital Twins in Automation of ICU Care 466

17.2.5 Summary 467

17.3 Role of Social-Behavioral Sciences 467

17.3.1 Introduction 467

17.3.2 Barriers to Innovation Adoption 467

17.3.3 Ergonomics and Codesign 468

17.3.4 Summary (Key Takeaways) 469

17.4 Future Research Challenges and Visions 470

17.4.1 Technology Vision of the Future of CPHS in ICU Care 470

17.4.2 Social-Behavioral Sciences Vision of the Future of CPHS in ICU Care 471

17.4.3 Joint Vision of the Future and Challenges to Overcome 473

17.5 Conclusions 473

References 474

Part V Sociotechnical Systems 491

18 Online Attention Dynamics in Social Media 493
Maria Castaldo, Paolo Frasca, and Tommaso Venturini

18.1 Introduction to Attention Economy and Attention Dynamics 493

18.2 Online Attention Dynamics 494

18.2.1 Collective Attention Is Limited 494

18.2.2 Skewed Attention Distribution 495

18.2.3 The Role of Novelty 496

18.2.4 The Role of Popularity 496

18.2.5 Individual Activity Is Bursty 499

18.2.6 Recommendation Systems Are the Main Gateways for Information 500

18.2.7 Change Is the Only Constant 500

18.3 The New Challenge: Understanding Recommendation Systems Effect in Attention Dynamics 501

18.3.1 Model Description 502

18.3.2 Results and Discussion 503

18.4 Conclusion 505

Acknowledgments 505

References 505

19 Cyber-Physical-Social Systems for Smart City 511
Gang Xiong, Noreen Anwar, Peijun Ye, Xiaoyu Chen, Hongxia Zhao, Yisheng Lv, Fenghua Zhu, Hongxin Zhang, Xu Zhou, and Ryan W. Liu

19.1 Introduction 511

19.2 Social Community and Smart Cities 513

19.2.1 Smart Infrastructure 513

19.2.2 Smart Energy 515

19.2.3 Smart Transportation 515

19.2.4 Smart Healthcare 517

19.3 CPSS Concepts, Tools, and Techniques 518

19.3.1 CPSS Concepts 518

19.3.2 CPSS Tools 519

19.3.3 CPSS Techniques 520

19.3.3.1 IoT in Smart Cities 520

19.3.3.2 Big Data in Smart Cities 525

19.4 Recent Research Advances 528

19.4.1 Recent Research Advances of CASIA 528

19.4.2 Recent Research in European Union 531

19.4.3 Future Research Challenges and Visions 533

19.5 Conclusions 537

Acknowledgments 538

References 538

Part VI Concluding Remarks 543

20 Conclusion and Perspectives 545
Anuradha M. Annaswamy, Pramod P. Khargonekar, Françoise Lamnabhi-Lagarrigue, and Sarah K. Spurgeon

20.1 Benefits to Humankind: Synthesis of the Chapters and their Open Directions 545

20.2 Selected Areas for Current and Future Development in CPHS 547

20.2.1 Driver Modeling for the Design of Advanced Driver Assistance Systems 547

20.2.2 Cognitive Cyber-Physical Systems and CPHS 547

20.2.3 Emotion-Cognition Interactions 548

20.3 Ethical and Social Concerns: Few Directions 549

20.3.1 Frameworks for Ethics 550

20.3.2 Technical Approaches 550

20.4 Afterword 551

References 551

Index 555

Authors

Anuradha M. Annaswamy Massachusetts Institute of Technology, Cambridge, MA, USA. Pramod P. Khargonekar University of California Irvine, CA, USA. Fran¿oise Lamnabhi-Lagarrigue CNRS, CentraleSupelec, University of Paris-Saclay, Gif-sur-Yvette, France. Sarah K. Spurgeon University College London, London, UK.