Intelligent Cyber-Physical Systems for Autonomous Transportation

Description

This book provides comprehensive discussion on key topics related to the usage and deployment of AI in urban transportation systems including drones. The book presents intelligent solutions to overcome the challenges of static approaches in the transportation sector to make them intelligent, adaptive, agile, and flexible. The book showcases different AI-deployment models, algorithms, and implementations related to intelligent cyber physical systems (CPS) along with their pros and cons. Even more, this book provides deep insights into the CPS specifically about the layered architecture and different planes, interfaces, and programmable network operations. The deployment models for AI-based CPS are also included with an aim towards the design of interoperable and intelligent CPS architectures by researchers in future. The authors present hands on practical implementations, deployment scenarios, and use cases related to different transportation scenarios. In the end, the design and research challenges, open issues, and future research directions are provided. Foreword Preface Contents Contributors Part I Overview of Transportation Systems 1 Transportation Systems 1.1 Background of Transportation Systems 1.1.1 Roads 1.1.2 Rails 1.1.3 Air 1.2 Growth of Transportation Industry 1.2.1 Importance of Transportation Industry 1.2.2 Growth Factors in Transportation Industry 1.3 Challenges to Transportation Industry 1.3.1 Social and Political Challenges to Transportation Industry 1.3.2 Technical Challenges to Transportation Industry 1.4 Impact of Intelligent Transportation Systems 1.4.1 Overview of ITS 1.4.2 Applications of ITS 1.4.3 Challenges Overcome by Intelligent Transportation Systems 1.5 Conclusion and Future Directions References 2 Future Autonomous Transportation: Challenges and Prospective Dimensions 2.1 Introduction 2.2 State-of-the-Art System on Autonomous Vehicles 2.3 Technical Feasibility 2.4 Autonomous Transportation on Earth 2.4.1 Challenges 2.5 Autonomous Transportation in Air 2.5.1 Challenges 2.6 Autonomous Transportation in Sea 2.6.1 Challenges 2.7 Potential Technologies for Autonomous Transportation Systems 2.7.1 Blockchain 2.7.2 Artificial Intelligence and Machine Learning 2.7.3 Edge/Fog Computing 2.8 Conclusion References Part II Artificial Intelligence 3 Artificial Intelligence 3.1 AI Conception 3.1.1 Cognitive AI 3.1.2 Machine Learning AI 3.1.3 Deep Learning AI 3.2 Need and Evolution of AI 3.2.1 Origin and Development About AI 3.2.2 Common Approaches and Technologies 3.2.2.1 Natural Language Processing (NLP) 3.2.2.2 Artificial Neural Networks (ANN) 3.2.2.3 Computer Vision 3.2.2.4 Expert System 3.2.3 Successive Cross-Field Solutions 3.2.3.1 Smart Manufacturing 3.2.3.2 Smart City 3.2.3.3 Smart Care 3.2.3.4 Smart Education 3.2.3.5 Smart Workflow 3.3 AI for Transportation Systems 3.3.1 Motivations 3.3.2 Application Status 3.3.2.1 Vehicle Identification 3.3.2.2 Vehicle Retrieval 3.3.2.3 Traffic Signal System 3.3.2.4 Driverless 3.3.3 Challenges References 4 Artificial Intelligence: Evolution, Benefits, and Challenges 4.1 Introduction to Artificial Intelligence 4.1.1 Types of Artificial Intelligence 4.2 Needs and Evolution 4.2.1 Needs of Artificial Intelligence 4.2.2 Evolution of Artificial Intelligence 4.3 AI for Transportation System 4.3.1 Introduction 4.3.2 Benefits of AI in Transportation System 4.3.3 Challenges and Future Research Directions References 5 Artificial Intelligence: Need, Evolution, and Applications for Transportation Systems 5.1 Overview of Artificial Intelligence 5.1.1 Evolution of Artificial Intelligence 5.1.2 Machine Learning 5.1.3 Reinforcement Learning 5.1.4 Other State-of-the-Art AI Algorithms 5.2 Artificial Intelligence-Empowered Transportation Network 5.2.1 Artificial Intelligence for V2X 5.2.2 Artificial Intelligence for Vehicular Edge Computing 5.2.3 Artificial Intelligence for Unmanned Aerial Vehicle 5.3 AI-Based Applications in Transportation System 5.3.1 Autonomous Driving 5.3.2 Traffic Prediction and Control 5.3.3 UAV Path Planning References 6 Artificial Intelligence Deployment in Transportation Systems 6.1 Review for AI-Deployment Transportation Systems 6.1.1 Overview 6.1.2 Prevalence 6.1.3 Development Status 6.2 Architecture for AI-Deployment Transportation Systems 6.2.1 AI-Deployment Sensing Layer 6.2.2 AI-Deployment Networking Layer 6.2.3 AI-Deployment Application Layer 6.3 Business Situations for AI-Deployment Transportation Systems 6.3.1 Autonomous Transportation Management 6.3.2 Vehicular Control 6.3.2.1 Intelligent Connected Car (ICV) 6.3.2.2 Intelligent Vehicle–Road Collaboration System (IVRCS) 6.3.2.3 Driverless 6.3.3 Public Transportation Scheduling 6.3.3.1 Public Transportation Scheduling Center 6.3.3.2 Sub-scheduling Center 6.3.3.3 Vehicle Mobile Station 6.3.3.4 Electronic Stop Sign 6.3.4 Transportation Information Service 6.3.4.1 Traveler Information Service Needs 6.3.4.2 The Content of Travel Information Services 6.3.4.3 Construction Content of Public Travel Information Service Platform 6.3.4.4 Travel Chain Analysis References Part III Cyber-Physical Systems 7 Cyber-Physical Systems: Historical Evolution and Role in Future Autonomous Transportation 7.1 Introduction 7.2 Internet of Things: Communication 7.3 Vehicle Communication 7.4 Applications 7.4.1 Intelligent Transportation 7.4.2 Supply Chain Management 7.5 Case Study 7.6 Connected Vehicle Challenges 7.7 Summary References 8 Cyber-Physical Systems in Transportation 8.1 Introduction to Cyber-Physical Systems 8.2 Design and Modeling 8.2.1 CPS Architecture 8.2.2 TCPS Architecture 8.3 The Roles of CPS in ITS 8.4 Challenges and Solutions 8.4.1 Standardization and Availability 8.4.2 Efficiency and Reliability 8.4.3 Security and Privacy 8.5 Concluding Remarks References Part IV Application Use Cases of Autonomous Transportation Systems 9 Correlation Between Traffic Lights and Emergency Vehicles in Intelligent Transportation System 9.1 Introduction 9.2 Architecture 9.2.1 Hardware Layer 9.2.1.1 Intrusive Sensors 9.2.1.2 Non-intrusive Sensors 9.2.2 Network Layer 9.2.3 Analytical Layer 9.2.4 Application Layer 9.3 Proposed Scheme 9.3.1 Centralised Light-Weight Reporting 9.3.2 Real-Time Route Information Dissemination to Emergency Vehicles 9.3.3 Multiple Emergency Vehicles Intersection Crossing 9.4 Conclusion and Future Scope References 10 Use Case for Underwater Transportation 10.1 Introduction 10.2 Related Work 10.3 Proposed Scheme 10.3.1 Network Architecture 10.3.2 Depth Threshold 10.3.3 Cluster Formation 10.3.4 Skipping Nodes 10.3.5 Inter-Cluster and Intra-Cluster Communication 10.3.6 Aggregation Techniques 10.3.6.1 Flow Chart 10.4 Conclusions and Future Work References 11 Advanced Signal Processing for Autonomous Transportation Big Data 11.1 Introduction 11.2 Related Work 11.3 Method 11.3.1 Industrial Big Data 11.3.2 Signal Processing Technology 11.3.3 Realization and Application of Autonomous Driving Based on Signal Processing 11.3.4 The Advanced Signal Processing System for Industrial Big Data 11.3.5 Simulation Analysis 11.4 Results and Discussions 11.4.1 Analysis of the Transmission Accuracy of Signal Processing 11.4.2 Performance Analysis of Different Algorithm Libraries 11.4.3 Performance Analysis of Packet Loss Rate in the Data Signal Processing System 11.5 Conclusions References 12 Deep Neural Network-Based Prediction of High-Speed Train-Induced Subway Track Vibration 12.1 Introduction 12.2 Deep Neural Network Model 12.2.1 Architecture 12.2.2 Model Training 12.3 Numerical Experiments 12.3.1 Searching for the Best Time Step 12.3.2 How Far in the Future Can We Correctly Predict? 12.3.3 Computing the Overall Accuracy Based on Error Margin 12.3.4 Training Times 12.3.5 Possible Applications of Proposed Deep Learning Vibration Estimation 12.4 Final Remarks References 13 Advanced Complex Data Analysis of Autonomous Transportation for Smart City Industrial Environment 13.1 Introduction 13.2 Related Works 13.3 Method 13.3.1 Architecture and Key Technologies of IIoT 13.3.2 Spectrum-Based SDN Deployment Algorithm 13.3.3 Industrial Complex Events and Data Processing 13.3.4 Independent Transportation Mode in Industrial Environment 13.3.5 Industrial Advanced Complex Data Analytics 13.3.6 Simulation Experiment of Industrial Complex Data Analytics 13.4 Results and Discussions 13.4.1 Comparison of Different Data Coordination Methods 13.4.2 Analysis of the Operation Effect of Complex Event Mode Based on Industrial Data 13.5 Conclusions References 14 A Meta Sensor-Based Autonomous Vehicle Safety System for Collision Avoidance Using Li-Fi Technology 14.1 Introduction 14.2 Literature Review 14.3 Proposed Framework 14.3.1 Proposed System Flowchart 14.3.2 Description of LV & FV Scenario and Li-Fi Communication 14.3.3 Case Study of the Need of the Meta Sensor 14.3.4 Importance of Brake Sensor 14.3.5 Meta Sensor 14.3.6 Proposed Algorithm 14.4 Simulation and Results 14.5 Conclusions References Part V Security Perspective in Intelligent Transportation Systems 15 Secure Information Transmission in Intelligent Transportation Systems Using Blockchain Technique 15.1 Introduction 15.1.1 Motivation and Research Objective 15.2 Related Work 15.3 Proposed Approach 15.3.1 IPFS Algorithm 15.4 Results and Analysis 15.5 Conclusion References 16 Privacy-Preserved Mobile Crowdsensing for Intelligent Transportation Systems 16.1 Introduction 16.2 Related Work 16.3 System Model 16.4 Crowdsensing Based on Federated Learning 16.4.1 Data Aggregation with Team Participation 16.4.2 Data Model Aggregation Process 16.4.3 Team Credit Management 16.5 Performance Evaluation 16.5.1 Simulation Setup 16.5.2 Experimental Results 16.5.2.1 Model Training 16.5.2.2 System Performance 16.6 Conclusion References Index