Connected and Autonomous Vehicles in Smart Cities

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editor
Contributors
Chapter 1 Connected and Autonomous Electric Vehicle Charging Infrastructure Integration to Microgrids in Future Smart Cities
1.1 Introduction
1.1.1 Smart Cities
1.1.2 Microgrids
1.1.3 Renewable Energy Generation Resources
1.1.3.1 SP
1.1.3.2 Wind Turbines
1.1.3.3 Mini-hydro
1.1.4 Energy Trading among Microgrids
1.2 CAEVs and the Effect of Integrating CAEVs to Microgrids
1.3 Microgrid Control Methods in the Presence of CAEVs
1.3.1 Microgrid Centralized Control
1.3.2 Microgrid Decentralized Control
1.3.3 Microgrid Distributed Control
1.4 Quality of Service in Plug-in Electric Vehicle Charging Infrastructure
1.5 Performance Evaluation
1.6 Challenges and Future Directions
1.7 Conclusion
References
Chapter 2 A Hierarchical Management Framework for Autonomous Electric Mobility-on-Demand Services
2.1 Introduction
2.1.1 Overview
2.1.2 21st Century Urban Transportation System
2.1.3 AEMoDs: Opportunities and Challenges
2.1.4 Contributions
2.2 Smart Dispatching and Routing of AEMoDs
2.2.1 Queuing Model
2.2.2 System Parameters
2.3 Lower Layer
2.3.1 Multiclass Charging and Dispatching
2.3.1.1 System Stability Conditions
2.3.1.2 Maximum Response Time Optimization Problem Formulation
2.3.1.3 Optimal Dispatching and Charging Decisions
2.3.1.4 Maximum Expected Response Time
2.3.1.5 Average Response Time Optimization Problem Formulation
2.3.1.6 Optimal Dispatching and Charging Decision
2.3.1.7 Simulation Results
2.3.2 Multiclass Dispatching with Subclass Charging
2.3.2.1 Stability Conditions
2.3.2.2 Subclass Charging and Dispatching Optimization Problem Formulation
2.3.2.3 Lower Bound Analytical Solutions
2.3.2.4 Simulation Results Analysis
2.4 Middle Layer: Optimal Vehicle Dimensioning
2.4.1 System Stability and Response Time Limit Conditions
2.4.2 Optimal Vehicle Dimensioning Problem Formulation
2.4.2.1 Lower-Bound Solution
2.4.2.2 Solution Tightening
2.4.3 Simulation Results
2.5 Upper Layer: Fleet Rebalancing with In-route Charging
2.5.1 System Stability Conditions
2.5.2 Maximum Response Time Optimization Problem Formulation
2.5.3 Optimal Rebalancing and Charging Decisions
2.5.4 Maximum Expected Response Time
2.5.5 Simulation Results
2.6 Conclusion
References
Chapter 3 Multifaceted Synthesis of Autonomous Vehicles’ Emerging Landscape
3.1 Introduction
3.2 Benefits
3.2.1 Safety
3.2.2 Vehicle Kilometers Traveled (VKT), Vehicle Ownership, and Congestion
3.2.3 Better Equity and Potential Job Loss
3.2.4 System Capacity
3.2.5 Parking Spaces
3.2.6 Energy and Emissions
3.2.7 System and Policies
3.2.8 Summary of AVs’ Benefits and Impacts
3.3 Economic Benefits and Societal Impacts
3.4 Implication of AVs on Travel Behavior and Development of Cities
3.4.1 Travel Behavior
3.4.2 Travel Cost and Affordability
3.4.3 Penetration Rate and Fleet Size
3.4.4 Parking Strategy and Demand
3.4.5 Summary of AV Implications
3.5 User Opinions, Adoption, and Perceptions
3.5.1 Effect of Awareness/Previous Experience on Public Opinion
3.5.2 Country/National Level Insights
3.5.3 Synopsis on Truck Drivers
3.5.4 Perceived Benefits and Implications
3.5.5 View of State of Technology
3.5.6 Effect of Socioeconomic and Demographics
3.5.7 Affordability and Willingness to Pay/Own
3.5.8 Summary of Public Opinions and Perception about AVs
3.6 Challenges Associated with AVs
3.6.1 Accuracy
3.6.2 Liability and Regulations
3.6.3 Ethics
3.6.4 AVs’ Accidents
3.7 Infrastructure Requirements and Implications
3.7.1 Traffic Management
3.7.2 Lane Marking and Signage
3.7.3 Potential Need for Safe Harbor Areas
3.7.4 Design of Parking for CAVs
3.7.5 Fuel and Power Distribution
3.7.6 Impact on Bridges
3.7.7 Internet and Connectivity
3.7.8 Road Geometry
3.8 Synopsis on Pilots and Laws and Regulations
3.9 New Value Network and New Business Models
3.10 Framework for Incorporating AVs into the Realm of Smart Cities
References
Chapter 4 Machine Learning Methodologies for Electric-Vehicle Energy Management Strategies: A Comprehensive Survey
4.1 Introduction
4.2 Rule-Based and Optimization-Based EMSs for EVs/HEVs
4.2.1 Rule-Based EMS for EVs/HEVs
4.2.2 Optimization-Based EMS for EVs/HEVs
4.3 Machine Learning-Based Tools for EV Energy Management
4.3.1 Prediction-Based EMS for EVs
4.3.2 Learning-Based EMS for EVs
4.4 Conclusions and Future Directions
Acknowledgment
Appendix A
References
Chapter 5 Dynamic Road Management in the Era of CAV
5.1 Introduction
5.1.1 Road Traffic Problems
5.1.2 Conventional and Emerging Congestion Mitigation Methodologies
5.1.3 Connected Vehicles and Infrastructure
5.1.4 Scope and Organization
5.2 DTM Challenges
5.2.1 Data Collection
5.2.2 Road Configuration
5.2.3 Communication and Control
5.2.4 Traffic Assignment
5.3 CAV-Enabled Traffic Management
5.3.1 Autonomous Intersection Management
5.3.2 Adaptive Traffic Light Control
5.3.3 Dynamic Lane Grouping
5.3.4 Dynamic Lane Reversal
5.3.5 Dynamic Trajectory Planning
5.4 Smart Road Vision and Practical Issues
5.4.1 Support of Human-Driven Vehicles
5.4.2 Optimized Route Selection in Mixed Traffic
5.5 Conclusion and Open Research Problems
Acknowledgment
References
Chapter 6 VANET Communication and Mobility Sustainability: Interactions and Mutual Impacts in Vehicular Environment
6.1 Introduction
6.2 Mobility and Traffic Engineering Fundamentals
6.2.1 Car-Following Behaviour
6.2.2 Traffic Modelling Techniques
6.2.3 Traffic Navigation
6.2.4 Eco-Routing Navigation
6.2.5 Eco-Routing Considering Ideal Communication
6.3 VANET Communication
6.3.1 VANET Characteristics
6.3.2 VANET Challenges
6.3.3 Physical Specifications of VANET
6.4 Modelling VANET Communication in Large-Scale Networks
6.4.1 Modelling the WAVE Medium Access Technique
6.4.1.1 MAC Operation in WAVE
6.4.1.2 Representing the System in Markov Model and Its Solution
6.4.2 MAC Queuing Using the M/M/1/K Model
6.4.3 Communication Model Validation
6.5 Modelling Eco-Routing and VANET
6.5.1 The INTEGRATION Software
6.5.2 Eco-Routing with Realistic VANET Communication Modelling
6.6 Simulation and Results
6.6.1 The Ideal Communication Case
6.6.1.1 Impact of Penetration Rate on Fuel Consumption
6.6.1.2 Penetration Rate and Congestion Levels
6.6.2 Realistic Communication Case
6.6.2.1 RSU Allocation
6.6.2.2 Communication System Impact on Eco-Routing System Performance
6.6.2.3 Quantifying the Realistic Communication Impact on Mobility Sustainability
6.6.2.4 Impact of Realistic Communication and Penetration Ratio
6.7 Conclusion
References
Chapter 7 Message Dissemination in Connected Vehicles
7.1 Background Work
7.1.1 Dissemination of Messages Using Vehicular Cloud Computing
7.1.2 Dissemination of Messages Using Vehicular Fog Computing
7.2 Hybrid-Vehcloud Message Dissemination
7.2.1 Dissemination of Messages Using Hybrid-Vehcloud
7.2.2 Hybrid-Vehcloud Message Dissemination Algorithm
7.3 DFCV Message Dissemination
7.3.1 DFCV Message Dissemination Technique
7.3.2 DFCV Message Dissemination Algorithm
7.4 Performance Evaluation
7.4.1 Simulation Setup
7.4.2 Performance Metrics
7.4.3 Performance Evaluation of Hybrid-Vehcloud
7.4.4 Performance Evaluation of DFCV
7.5 Comparison of Vehicular Fog Computing and Vehicular Cloud Computing
7.5.1 Advantages of Vehicular Fog Computing over Vehicular Cloud Computing
7.5.2 Advantages of Vehicular Cloud Computing over Vehicular Fog Computing
7.6 Conclusion
7.7 Future Directions
References
Chapter 8 Exploring Cloud Virtualization over Vehicular Networks with Mobility Support
8.1 Introduction
8.2 Vehicular Networks
8.2.1 Mobility Management
8.3 Softwarization and Virtualization
8.4 System Design
8.5 Use Cases
8.5.1 SURROGATES
8.5.1.1 Concept
8.5.1.2 Operation
8.5.1.3 Implementation
8.5.1.4 Performance Evaluation
8.5.2 CAVICO
8.5.2.1 Concept
8.5.2.2 Operation
8.5.2.3 Performance Evaluation
8.5.3 VRU-Safe
8.5.3.1 Concept
8.5.3.2 Implementation
8.5.3.3 Performance Evaluation
8.5.4 5G-CAGE
8.5.4.1 Concept
8.5.4.2 Implementation
8.5.4.3 Performance Evaluation
8.6 Research Challenges
8.7 Conclusions
Acknowledgment
References
Chapter 9 Data Offloading Approaches for Vehicle-to-Everything (V2X) Communications in 5G and Beyond
9.1 Introduction
9.2 A Brief Overview of 5G, V2X Communications and Applications
9.3 Challenges on Resource Allocation and Motivation
9.4 Possible Solution Approaches
9.5 Workload Offloading Approaches for V2X Communications in 5G
9.5.1 Computation Offloading
9.5.2 Data Traffic Workload Offloading
9.5.2.1 Centralized/Cloud-Based Workload Offloading
9.5.2.2 Distributed Based Workload Offloading
9.5.2.3 Selected Vehicle-Based Workload Offloading
9.6 Summary
References
Chapter 10 Connected Unmanned Aerial Vehicles for Flexible Coverage, Data Gathering and Emergency Scenarios
10.1 Introduction
10.2 Architectures for Networks of Drones
10.2.1 Centralized-Mesh Network Architecture
10.2.2 UAV Ad Hoc Network Architecture
10.2.3 Multi-Group UAV Network Architecture
10.2.4 Multi-Layer UAV Ad Hoc Network Architecture
10.3 Simulation Setup
10.4 Performance Evaluation and Discussion
10.5 Conclusion
Acknowledgement
References
Chapter 11 Localization for Vehicular Ad Hoc Network and Autonomous Vehicles, Are We Done Yet?
11.1 Introduction
11.2 Standard Localization System in CAVs
11.3 Perception and Sensors
11.3.1 Ultrasound Technology
11.3.2 Front and Rear Radar Sensors
11.3.3 Camera and Visual Sensor
11.3.4 LiDAR, Laser and Infrared Sensors
11.3.5 Autonomous Vehicle Networks
11.4 Localization System Designs for Autonomous Driving
11.4.1 Global Positioning System (GPS)
11.4.2 Map Matching
11.4.2.1 Incremental Method
11.4.2.2 Global Method
11.4.2.3 Statistical Method
11.4.2.4 Fuzzy Logic-Based Algorithms
11.4.3 Cellular Localization
11.4.3.1 Ranging Method
11.4.3.2 Limitation of Cellular Localization Accuracy
11.4.4 Image and Video Localization Technique
11.4.5 Dead Reckoning
11.4.6 Distributed Ad Hoc Localization
11.5 Simultaneous Localization and Mapping (SLAM)
11.5.1 Filtering Solutions
11.5.2 Monte Carlo Solutions
11.5.3 Smoothing and Mapping (Loop Closure)
11.5.4 iSAM and GraphSLAM
11.6 Cooperative Estimation, Filtering, and Sensor Fusion
11.7 Localization Systems in Use for Autonomous Driving
11.7.1 Application for Accurate Location-Aware
11.7.2 Cooperative Intersection Safety Applications
11.7.3 Cooperative Adaptive Cruise Control Application
11.7.4 Platooning Application
11.7.5 Application for High Accurate Location-Aware
11.7.6 Application for Inaccurate Location-Aware
11.7.7 VETRAC
11.8 Conclusion
References
Chapter 12 Automotive Radar Signal Analysis
12.1 Automotive Radar
12.1.1 Assisted Driver Sensors
12.1.2 Significance
12.2 Waveforms in Automotive Radars
12.2.1 Triangular FMCW Waveform
12.2.1.1 Triangular FMCW Simulation
12.2.2 Trapezoidal FMCW Waveform
12.2.3 Sawtooth FMCW Waveform
12.2.3.1 Range, Velocity and Angle Estimation
12.2.4 FMCW System Resolution and Performance
12.2.4.1 Range Resolution
12.2.4.2 Velocity Resolution
12.2.4.3 Angle Resolution
12.2.5 OFDM Waveform
12.2.5.1 System Model
12.2.5.2 Range and Velocity Estimation
12.2.5.3 Waveform Construction
12.2.5.4 OFDM Simulation
12.3 MIMO Radar
Summary
Triangular FMCW Matlab Simulation Code
References
Chapter 13 Multisensor Precise Positioning for Automated and Connected Vehicles
13.1 Positioning of Automated Vehicles
13.2 Different Sensors for AV/CV Positioning
13.2.1 Global Navigation Satellite Systems
13.2.2 Inertial Navigation Systems
13.2.3 Odometers
13.2.4 Perception Systems
13.2.5 Sensors Utilization under Different Driving Conditions
13.3 Multisensor Fusion for Positioning
13.3.1 Multisensor Fusion Filters
13.3.1.1 The Kalman Filter
13.3.1.2 The Particle Filter
13.3.2 Multisensor Fusion Architectures
13.3.2.1 GNSS/INS Integration Architectures
13.3.2.2 General Integration Architectures
13.4 PPP/INS Integration: A Case Study for Automated Level 2 Driving
13.4.1 Precise Point Positioning
13.4.1.1 Standard Dual-frequency PPP
13.4.1.2 Single-Frequency PPP
13.4.2 SF-PPP/INS Integration
13.4.2.1 Methodology
13.4.2.2 Results and Discussion
13.5 Summary
References
Chapter 14 Deploying Wireless Charging Systems for Connected and Autonomous Electric Vehicles
14.1 Introduction
14.2 Preliminary
14.2.1 Connected and Autonomous Electric Vehicles
14.3 Wireless Charging Systems
14.3.1 Types of Wireless EV Charging
14.4 Standardization for Wireless EV Charging
14.4.1 ISO/IEC 15118 Standard
14.4.1.1 Wireless Communication Requirements
14.4.2 SAE J2954 Standard
14.4.2.1 Overviews of J2954 WPT System and Charging Process
14.4.3 Other WEVC Standards
14.4.3.1 IEC 61980 Standards
14.4.3.2 ISO/PAS 19363 Standard
14.4.3.3 SAE J2847/6 Standard
14.5 CAEV Charging Management System
14.5.1 Automated Reservation Mechanism
14.5.2 SecCharge Test Bed for SWC System
14.6 Summary
Acknowledgment
References
Chapter 15 Dynamic Wireless Charging of Electric Vehicles
15.1 Introduction
15.2 Literature Review
15.3 Electric Vehicles and Dynamic Charging
15.4 Wireless Power Transfer (WPT)
15.4.1 WPT Principles
15.4.2 WPT System
15.4.3 Analysis of WPT
15.5 Dynamic Wireless Power Transfer
15.5.1 DWPT System
15.5.2 DWPT Analysis
15.5.3 Design Considerations
15.6 Optimal Performance of DWPT Systems
15.6.1 Maximum Efficiency and Optimal Frequency
15.6.2 Load Matching
15.7 Control System
15.7.1 Output Voltage Regulation
15.7.2 Maximum Efficiency Achievement
15.7.2.1 Resonant Parameter Independent Method
15.7.2.2 Coupling Coefficient Estimation-Based Method
15.8 Future Outlook
Acknowledgment
References
Chapter 16 Wirelessly Powered Unmanned Aerial Vehicles (UAVs) in Smart City
16.1 Introduction
16.2 UAVs in Smart City
16.3 Energy Efficiency and Connectivity of UAVs
16.4 Wireless Power Transfer for UAVs
16.5 WPT-BPL for UAVs
16.5.1 Induction of magnetically coupled circuit
16.5.2 Design Issues and Performance Analysis
16.5.2.1 Output Power Analysis
16.5.2.2 Design for Broadband Communication
16.5.3 2D and 3D Power and Data Channel
16.6 Conclusion
Acknowledgment
References
Chapter 17 Cyber Security Considerations for Automated Electro-Mobility Services in Smart Cities
17.1 Introduction
17.2 Connected and Autonomous Electric Vehicles
17.3 Automated Electro-Mobility Services
17.3.1 Peer-to-Peer Car-Sharing Services
17.4 Cyber Security for AEM Services
17.4.1 Security Goals
17.4.2 Potential Threats
17.4.3 Security Requirements
17.5 Proposed Security Solution for P2P Car-Sharing System
17.5.1 Issues and Challenges
17.5.2 Security Considerations
17.5.3 Conjugated Authentication and Authorization
17.5.3.1 Entity Authentication
17.5.3.2 Authenticated Prior Binding
17.5.4 Token-Based Authentication and Authorization
17.5.4.1 Token Generation Phases
17.5.4.2 Validation Phase
17.6 Summary
Acknowledgment
References
Chapter 18 Incentivized and Secure Blockchain-based Firmware Update and Dissemination for Autonomous Vehicles
18.1 Introduction
18.1.1 Contributions
18.2 Literature Review
18.3 Preliminaries
18.3.1 Blockchain and Smart Contracts
18.3.2 Cryptographic Tools
18.3.2.1 Attribute-Based Encryption
18.3.2.2 Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK)
18.3.2.3 Aggregate Signatures
18.4 Proposed System
18.4.1 System Architecture
18.4.2 System Initialization
18.4.3 Smart Contract Creation
18.4.4 Firmware Update Dissemination
18.4.5 Rewarding
18.5 Performance Evaluations
18.5.1 On-Chain Cost
18.5.1.1 Methodology/Experiment Setup
18.5.1.2 Performance Metrics
18.5.1.3 Results and Discussion
18.5.2 Off-Chain Cost
18.6 Security Analysis
18.7 Conclusion
Acknowledgment
References
Index