Intelligent Cyber-Physical Systems for Autonomous Transportation:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2022
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Internet of Things Series
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (289 Seiten) |
| ISBN: | 9783030920548 |
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| 505 | 8 | |a Intro -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 16.5.2 Experimental Results | |
| 650 | 4 | |a Intelligent transportation systems | |
| 650 | 4 | |a Urban transportation | |
| 700 | 1 | |a Aujla, Gagangeet Singh |e Sonstige |4 oth | |
| 700 | 1 | |a Kaur, Kuljeet |e Sonstige |4 oth | |
| 700 | 1 | |a Hassan Ahmed Shah, Syed |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Garg, Sahil |t Intelligent Cyber-Physical Systems for Autonomous Transportation |d Cham : Springer International Publishing AG,c2022 |z 9783030920531 |
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| author | Garg, Sahil |
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| building | Verbundindex |
| bvnumber | BV049019486 |
| collection | ZDB-30-PQE |
| contents | Intro -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC6965115 (ZDB-30-PAD)EBC6965115 (ZDB-89-EBL)EBL6965115 (OCoLC)1313385482 (DE-599)BVBBV049019486 |
| dewey-full | 388.312 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 388 - Transportation |
| dewey-raw | 388.312 |
| dewey-search | 388.312 |
| dewey-sort | 3388.312 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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-- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 &amp -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">16.5.2 Experimental Results</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligent transportation systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Urban transportation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield 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| id | DE-604.BV049019486 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:13:39Z |
| indexdate | 2024-07-10T09:52:58Z |
| institution | BVB |
| isbn | 9783030920548 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034282393 |
| oclc_num | 1313385482 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (289 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Internet of Things Series |
| spelling | Garg, Sahil Verfasser aut Intelligent Cyber-Physical Systems for Autonomous Transportation 1st ed Cham Springer International Publishing AG 2022 ©2022 1 Online-Ressource (289 Seiten) txt rdacontent c rdamedia cr rdacarrier Internet of Things Series Description based on publisher supplied metadata and other sources Intro -- 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 Intelligent transportation systems Urban transportation Aujla, Gagangeet Singh Sonstige oth Kaur, Kuljeet Sonstige oth Hassan Ahmed Shah, Syed Sonstige oth Erscheint auch als Druck-Ausgabe Garg, Sahil Intelligent Cyber-Physical Systems for Autonomous Transportation Cham : Springer International Publishing AG,c2022 9783030920531 |
| spellingShingle | Garg, Sahil Intelligent Cyber-Physical Systems for Autonomous Transportation Intro -- 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 Intelligent transportation systems Urban transportation |
| title | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_auth | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_exact_search | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_exact_search_txtP | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_full | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_fullStr | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_full_unstemmed | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_short | Intelligent Cyber-Physical Systems for Autonomous Transportation |
| title_sort | intelligent cyber physical systems for autonomous transportation |
| topic | Intelligent transportation systems Urban transportation |
| topic_facet | Intelligent transportation systems Urban transportation |
| work_keys_str_mv | AT gargsahil intelligentcyberphysicalsystemsforautonomoustransportation AT aujlagagangeetsingh intelligentcyberphysicalsystemsforautonomoustransportation AT kaurkuljeet intelligentcyberphysicalsystemsforautonomoustransportation AT hassanahmedshahsyed intelligentcyberphysicalsystemsforautonomoustransportation |