11063nam 2200493 450 991075508390332120231114005127.0981-9954-39-8(MiAaPQ)EBC30847525(Au-PeEL)EBL30847525(PPN)272920231(EXLCZ)992864534930004120231114d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCommunication, Computation and Perception Technologies for Internet of Vehicles /Yongdong Zhu [and three others], editorsFirst edition.Hangzhou, China :Springer Nature Singapore Pte Ltd.,[2023]©20231 online resource (294 pages)Print version: Zhu, Yongdong Communication, Computation and Perception Technologies for Internet of Vehicles Singapore : Springer,c2023 9789819954384 Includes bibliographical references.Intro -- Acknowledgement -- Contents -- 1 Modeling Microscopic Traffic Behaviors for Connected and Autonomous Vehicles -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Car-Following Models -- 2.2 Modeling Traffic Instabilities -- 3 The Proposed Model -- 3.1 The Congestion Term -- 3.2 The Free-Flow Term -- 4 Car-Following Behaviors in Mixed Traffic Flow -- 4.1 Car-Following Behaviors of AVs -- 4.2 Car-Following Behaviors of HVs -- 5 Vehicle Platoon Simulation for Mixed Traffic Flow -- 6 Conclusion -- References -- 2 ITS Traffic Management with Connected Vehicles: An Overview -- 1 Introduction -- 2 Traffic Sensing with Connected Vehicles -- 2.1 The Evolution of ITS Data Collection Techniques -- 2.2 Traffic State Estimation -- 3 Traffic Management with Connected Vehicles -- 3.1 Adaptive Traffic Control and Guidance -- 3.2 Bottleneck Capacity Optimization -- 4 Open Issues and Future Research Directions -- 4.1 The Impact of CV Penetration Rate -- 4.2 Robustness of ITS Solutions Under Connected Environment -- 5 Conclusions -- References -- 3 Evolution of Wireless Communication Technology for V2X Assisted Autonomous Driving -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 A Brief History of Wireless Communication Technology for V2X -- 3 Use Case and Requirements of V2X Communications -- 3.1 3GPP -- 3.2 5GAA -- 4 Wireless Communication Technology for V2X -- 4.1 Bluetooth -- 4.2 ZigBee -- 4.3 Wi-Fi -- 4.4 DSRC -- 4.5 C-V2X -- 5 Standardization Activities for V2X Communications -- 6 Conclusion -- References -- 4 5G Meets V2X: Integration, Application, Standard and Industrialization -- 1 Introduction -- 2 5G and Its Integration with V2X -- 2.1 5G Introduction -- 2.2 5G Three Scenarios and Standardization.2.3 5G URLLC Scenario, Characteristics, Challenges -- 2.4 Principle of 5G uRLLC Integration with V2X -- 2.5 5G Applications for V2X -- 2.6 V2X Security Applications -- 2.7 Transportation Efficiency Applications -- 2.8 V2X Entertainment Applications -- 2.9 Future V2X Applications -- 3 Standardization and Industrialization of C-V2X -- 3.1 C-V2X Technology and Standardization -- 3.2 Global Spectrum Allocation for C-V2X -- 3.3 C-V2X Technology Application Trends and Industrialization -- 4 Conclusion -- References -- 5 Enabling Reconfigurable Intelligent Surface for V2X Communication Systems -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 V2X Technology and Standard Evolution -- 2.2 Fundamentals and Introduction of RIS -- 3 Modeling and Application of RIS-Assisted Communication System -- 3.1 Received Signal Modeling of RIS-Assisted Communication System -- 3.2 Potential Application Scenarios of RIS-Assisted Communication System -- 4 RIS-Assisted V2X Communication System -- 4.1 Challenge of RIS-Assisted V2X Communication -- 4.2 RIS-Assisted Vehicular Communication Network -- 4.3 RIS-Assisted Vehicular Edge Computing -- 5 Future Directions -- 6 Conclusion -- References -- 6 Real-Time Object Detection for ITS Applications -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Object Detection Algorithm -- 2.1 Overview of Object Detection Algorithm -- 2.2 YOLOv5 Algorithm -- 3 Lightweight Method -- 3.1 Lightweight Backbone -- 3.2 Model Compression -- 4 Knowledge Distillation Strategy -- 4.1 Knowledge Distillation -- 4.2 Knowledge Distillation Based on YOLOv5 -- 5 Experiments and Discussions -- 5.1 Datasets -- 5.2 Experiment Environment -- 5.3 Evaluation Metrics -- 5.4 Model Training -- 5.5 Experimental Results -- 6 Conclusion -- References.7 3D Scene Perception for Autonomous Driving -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Existing LiDAR Sensor Based Scene Perception Methods -- 2.2 Existing Vision Based Scene Perception Methods -- 2.3 Existing LiDAR Sensor and Vision Fusion Methods -- 3 Vision Based 3D Scene Perception -- 3.1 Overview -- 3.2 Depth Estimation with a Single Image -- 3.3 Depth Estimation with a Stereo Pair -- 3.4 Depth Estimation with a Monocular Video -- 4 Datasets for Evaluation -- 4.1 KITTI -- 4.2 NuScenes -- 4.3 Quality Measure -- 5 Future Directions -- 5.1 Temporal Information Exploration -- 5.2 Fusion with Cheap Radar -- 6 Conclusion -- References -- 8 Multi-sensor Fusion for Perception in Complex Traffic Environments -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Cooperative Perceptions -- 2.2 Tasks -- 2.3 Data Representations for LiDAR and Image -- 3 Fusion Methodology -- 3.1 Decision-Level Fusion -- 3.2 Decision-Feature-Level Fusion -- 3.3 Feature-Level Fusion -- 4 Future Directions -- 4.1 Information Loss Due to Heterogeneous Dimensions -- 4.2 Misalignment Due to Noisy Projection Matrix -- 4.3 Conflicts Due to Data Resolution -- 5 Conclusion -- References -- 9 A Cooperative Positioning Enhancement for Blockchain-Enabled Vehicular Networks -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Related Works -- 2.1 Machine Learning Applied in Vehicle Positioning -- 2.2 Blockchain Technology for Internet of Vehicles -- 3 System Model -- 3.1 Positioning Scenarios -- 3.2 Feasibility Analysis of Sharing Positioning Error -- 4 Positioning Error Prediction Algorithm -- 4.1 Design of the DNN Algorithm -- 4.2 EPSO Optimized DNN Algorithm -- 4.3 Positioning Accuracy Enhancement Method.5 Blockchain and Smart Contracts -- 5.1 Vehicular Blockchain -- 5.2 Secure Data Sharing Scheme for Enhanced Positioning Accuracy -- 6 Simulation and Conclusion -- 6.1 Positioning Error Prediction and Correction -- 6.2 Comparison Between Different Methods -- 6.3 Conclusion -- References -- 10 Cooperative Cloud-Edge Computing for Integrated Sensing and Communication in Internet of Vehicles -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Intelligent Computing -- 2.2 ISAC for Cooperative Cloud-Edge Computing -- 3 Integrated Sensing and Communication for Cooperative Cloud-Edge Computing in Internet of Vehicles -- 3.1 Collaborative Cloud-Edge-End Computing for Deep Learning Training and Inference -- 3.2 Joint Communication and Computation Resource Allocation in Cooperative Cloud-Edge Computing Internet of Vehicles -- 4 Future Directions -- 4.1 Robustness, Stability and Security -- 4.2 Multi-agent Reinforcement Learning -- 5 Conclusion -- References -- 11 Big Data for Internet of Vehicles and Smart Transportation -- 1 Introduction -- 2 Background of Big Data -- 2.1 Big Data Origin/Features and Data Mining -- 2.2 Software Infrastructure of Big Data -- 2.3 Hardware Infrastructure of Big Data -- 3 Vehicle and Transportation Big Data Platform -- 3.1 Overview of Vehicle and Transportation Big Data Platform -- 3.2 Vehicle and Transportation Data Source Layer -- 3.3 Vehicle and Transportation Data Aggregation Layer -- 3.4 Vehicle and Transportation Data Storage Layer -- 3.5 Vehicle and Transportation Data Processing Layer -- 3.6 Vehicle and Transportation Data Analysis and Management Layer -- 3.7 Vehicle and Transportation Data Application Layer -- 4 Application of Big Data in Vehicle and Transportation Industry -- 4.1 Application of Big Data in Internet of Vehicles.4.2 Digital Twin Applications for Internet of Vehicles and Intelligent Transportation -- 5 Conclusion -- References -- 12 Noval Enabling Technology for V2X Network: Blockchain -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Blockchain Technology -- 2.2 V2X -- 3 Blockchain for V2X Scenarios -- 3.1 Trust Management -- 3.2 Data Sharing -- 3.3 Resource Sharing -- 3.4 Trading Application -- 3.5 Collaboration Application -- 3.6 Transportation Management -- 3.7 Evidence Service -- 4 Future Directions -- 4.1 Blockchain Performance -- 4.2 Quantum Attacks -- 4.3 Heterogeneous Service -- 4.4 Incentive Mechanism -- 4.5 Integration with Emerging Technologies -- 5 Conclusion -- References -- 13 An Introduction to Trust Management in Internet of Vehicles -- 1 Introduction -- 2 Security Problems in IoVs -- 2.1 Characteristics of IoVs -- 2.2 Security Requirements of IoVs -- 2.3 Common Malicious Attacks in IoVs -- 3 Taxonomy of Trust Management in IoVs -- 3.1 Entity-Centric Trust Model -- 3.2 Data-Centric Trust Model -- 3.3 Hybrid Trust Model -- 4 Simulation Tools for IoVs -- 4.1 Veins -- 4.2 Eclipse MOSAIC -- 4.3 The ONE Simulator -- 5 Future Research Opportunities -- 5.1 Social Relationships in IoVs -- 5.2 Development of Standard Evaluation Procedures and Datasets -- 5.3 Practical Issues of IoV Trust Models -- 6 Conclusion -- References -- 14 Misbehaviour Detection Mechanisms in Internet of Vehicles -- 1 Introduction -- 2 Threats Against Internet of Vehicles -- 2.1 Threats to the In-Vehicle Environment -- 2.2 Threats to the Wireless Communication Environment -- 3 Detection Mechanisms Classification -- 3.1 Detection Mechanisms Based on Message Content -- 3.2 Detection Mechanisms Based on Message Processing Behaviour -- 3.3 Detection Mechanisms Combined with Sensor -- 4 Further Discussions.5 Conclusion.Internet of thingsVehicular ad hoc networks (Computer networks)Internet of things.Vehicular ad hoc networks (Computer networks)004.678Zhu YongdongMiAaPQMiAaPQMiAaPQBOOK9910755083903321Communication, Computation and Perception Technologies for Internet of Vehicles3592060UNINA