Autonomous and Connected Heavy Vehicle Technology

Front Cover
Autonomous and Connected Heavy Vehicle Technology
Copyright
Contents
Contributors
Preface
Section 1: Review articles
Chapter 1: Lightweight and heavyweight technologies for autonomous vehicles: A survey
1. Lightweight sensor technology for automated and connected heavy vehicles
1.1. Lightweight and heavyweight sensors for vehicular technology
1.2. Lightweight and heavyweight sensor quality and data handling issues
1.3. Economic issues for automated technologies
1.4. Regulation for lightweight and heavyweight sensors-based automated technologies
1.5. Future scope and research challenges in lightweight and heavyweight technologies
2. Lightweight and heavyweight road safety issues for automated vehicles
3. Impact of heavy vehicle technologies with industry 4.0 standards
3.1. Industry 4.0 technologies
3.2. Heavy vehicle technology with artificial intelligence
3.3. Heavy vehicle technology with cloud, fog, and edge computing
4. Conclusion and future scope
References
Chapter 2: Cybercrimes and defense approaches in vehicular networks
1. Introduction
1.1. Defense working trends
1.2. Wireless networks in defense landscape
1.3. Cyberattacks in defense landscape
1.4. Automated vehicle network
1.4.1. Vehicle monitoring system
1.4.2. Automated vehicle tracking system
1.4.3. Relay in real time
1.4.4. No relay report later
1.5. Wireless networks
1.6. Future challenges
2. Literature review of cybersecurity and cyberattacks in defense networks
2.1. Data breach attacks during the Covid-19 pandemic
2.2. Types of cyberattacks
2.2.1. Malicious domains
2.2.2. Malware
2.2.3. Ransomware
2.3. Common data breach cyberattacks
2.3.1. Malware-based faked and spam Covid-19 reports
2.3.2. Email-based attacks
2.3.3. Message-based attacks
2.3.4. Fake mobile applications
2.3.5. UPI frauds
2.3.6. Twitter hack
2.3.7. Marriott data breach
2.3.8. Zoom credentials up for sale
2.3.9. Magellan Health (ransomware attack and data breach)
2.4. Cyberattack worldwide report in 2020
2.4.1. Worldwide yearly cyberattacks
2.4.2. Most cyberattacking sector
2.4.3. Most sensitive cyberattacking country
2.4.4. World professional cyberattackers by country
2.4.5. Cybersecurity increase in budgets
3. Methodology for securing data from cyberattacks
3.1. Application security issues and methodologies
3.1.1. Authentication
3.1.2. Authorization
3.1.3. Encryption
3.2. Information security issues and methodologies
3.3. Network security issues and methodologies
4. Data security measures
4.1. Manage social media profile security
4.2. Check privacy and security settings
4.3. Avoid opening and delete suspicious email or attachments
4.4. Keep software updated
5. Cybersecurity in defense networks
5.1. National defense networks
5.2. Cybersecurity in military networks
5.3. Cybersecurity in air networks
5.4. Cybersecurity in naval networks
6. Conclusion and future scope
References
Chapter 3: Autonomous driving systems and experiences: A comprehensive survey
1. Introduction
1.1. Classifications of autonomous vehicles
1.2. Benefits of autonomous vehicles
1.3. 2D and 3D object detection systems in autonomous vehicles
1.4. Simultaneous localization and mapping issues in driving
1.5. Autonomous driving system and future directions
2. Autonomous vehicles datasets and features
2.1. KITTI object detection dataset
2.2. Cityscape dataset
2.3. Mapillary Vistas Dataset
2.4. ApolloScape dataset
2.5. NuScenes dataset
2.6. Comparative analysis of autonomous vehicle datasets and their features
3. Lane detection system in autonomous vehicles
3.1. Issues and challenges in vision-based lane detection and analysis systems
3.2. Comparative analysis of vision-based end-to-end lane detection systems
3.3. Road planning and object detection systems for autonomous vehicles
3.4. Decision-making systems for autonomous vehicles
4. Autonomous vehicle movement systems
4.1. Optimal trajectory generation for dynamic street scenarios
4.2. Path planning and challenges in autonomous vehicles
4.3. Local and remote path planning challenges for off-road autonomous driving
4.4. Motion planning for on-road autonomous vehicles
4.5. Real-time autonomous vehicles movement and control techniques
4.6. Driving situations and vehicle path planning strategies
5. Conclusion
References
Chapter 4: Applications of blockchain in automated heavy vehicles: Yesterday, today, and tomorrow
1. Introduction
1.1. Blockchain for automated vehicles
1.2. Record keeper for on-road automated vehicles
1.3. Security measures for on-road automated vehicles
1.4. Blockchain security enhancements for on-road automated vehicle systems
1.5. Verification and validation (V&V) approaches for on-road automated vehicle systems
1.6. Automated driving systems and future directions
2. IoT devices and automated vehicles
2.1. IoT for better safety scenario
2.2. Facilities provided in automated vehicles
2.3. Predictive maintenance
2.4. Improving traffic conditions
3. Security verification and analysis process
3.1. Issues and challenges in blockchain networks
3.2. Protection against active and passive attacks
3.3. Intrusion detection and prevention mechanisms
3.4. Data tampering resistance measures
3.5. Formal security verification processes for automated vehicles
3.6. Public, private, and consortium/federated blockchain technologies for automated vehicles
4. Use case for blockchain-based automated vehicle management
5. Conclusion
References
Chapter 5: Eco-routing navigation systems in electric vehicles: A comprehensive survey
1. Introduction
1.1. Electric vehicle and factors affecting its acceptability
1.1.1. Electric vehicle
1.1.2. Range of electric vehicles
1.1.3. Range anxiety
2. Eco-routing of electric vehicles
2.1. Definition
2.2. Motivation for eco-routing
2.3. Future of eco-routing
2.4. Current primary eco-routing methods
3. Survey of literature
3.1. Electric vehicle routing problem
3.2. Electric vehicle energy consumption models
3.2.1. Factors affecting energy consumption in EVs
Road-related information
Aerodynamic drag losses
Rolling resistance
Road elevation
Traffic information
Battery related information
Internal resistance
Open circuit voltage (OCV)
Battery capacity
Coulombic efficiency
Temperature effect
Aging effect
3.2.2. Generalized models
Road load model
Powertrain loss model
Regenerative braking model
Cruise control model
Traction control model
Battery model
4. Range determination in electric vehicles
5. Existing eco-routing system prototypes
5.1. Depending on the speed profiles
5.2. Depending on historical and real-time traffic information
5.3. Based on GPS and fuel consumption data
5.4. Depending on time of travel and the route energy consumption
6. Major challenges
7. Proposed eco-routing system
8. Future scope
9. Conclusion
Acknowledgment
References
Section 2: Implementation or Simulation-based study for heavy vehicles technologies
Chapter 6: Automatic vehicle number plate detection and recognition systems: Survey and implementation
1. Introduction
2. Survey of automated vehicle number detection systems
3. Number detection system methodology
3.1. Vehicle detection
3.2. Number plate detection
3.3. Character segmentation
3.4. Character recognition
4. Distributed computing platform for automated number detection
5. Proposed automated vehicle number detection systems
5.1. Datasets
5.2. Experiment and evaluation
6. Conclusion and future scope
References
Chapter 7: A secured IoT parking system based on smart sensor communication with two-step user verification
1. Introduction
1.1. Internet of Things in transport management
1.1.1. V2V communication
1.1.2. Major challenges/issues
2. Existing system
3. EcoSystem: Internet of Things
3.1. Applications
3.1.1. Farming
3.1.2. Home automation
3.1.3. Healthcare
3.1.4. Retail
3.1.5. Industrial applications
3.2. Smart sensors
3.2.1. Position sensor
3.2.2. LIDAR (light imaging detection and ranging sensor)
3.2.3. Fuel sensor
3.2.4. Speed sensor
3.2.5. Infrared sensor (IR)
3.2.6. Image sensor
3.3. Arduino microcontroller
3.4. Merits and demerits of smart parking system (SPS)
3.4.1. Disadvantages
4. Proposed smart parking system
4.1. System architecture
4.2. Working procedure
4.3. Proposed algorithm: VirtualParking
5. Cloud computing
5.1. Features of cloud computing
6. Privacy-preserving smart parking system
6.1. Data privacy and preservation
6.1.1. Data perturbation
6.1.2. Data restriction techniques
6.2. Data aggregation
6.2.1. Mechanism
Forest-based
Unified aggregation
Clustering
6.3. Security attacks in IoT era
6.4. Key note on radio frequency identification
7. Networks and security
7.1. A role for WSN in parking management
7.2. Cryptography
7.3. Summary of crypt-based parking system
8. Discussion
9. Conclusion
References
Further reading
Chapter 8: Man-and-wife coupling and need for artificially intelligent heavy vehicle technology in The Long, Long Trailer
1. Argument and comparative methodology
2. Ethical and moral imperatives
3. Film at the intersection of technology, art, and material culture
4. Imaginary characters, real stars
5. Film adaptation of literary biography
6. Marriage as a connected vehicle
7. Rocky Mountain imagery in film art and AI for HVT
8. Missing: A catalytic converter
9. State of the art in artificial intelligence
10. Narratological framework and imagery
11. High technology and middle class daydreamers
12. Connected HVT, disconnected civilians
13. Measuring space and time
14. At the intersection: The artificiality of AI
15. Climbing to the top in a connected heavy vehicle
16. Romantic comedy of descent
17. Collision and disaster at the family reunion
18. Coupling and connectivity
19. Loves chemistry, lifes gravity
20. Loves Rocky overload: Dangerous deception
21. Conclusion
References
Further reading
Chapter 9: Pulse oximeter-based machine learning models for sleep apnea detection in heavy vehicle drivers
1. Introduction
2. Literature survey
3. Methodology
3.1. Objective (a)
3.2. Objective (b)
3.3. Objective (c)
3.4. Objective (d)
4. Experimental setup
5. Results and discussion
6. Conclusion and future scope
References
Chapter 10: Using wavelet transformation for acoustic signal processing in heavy vehicle detection and classification
1. Introduction
2. Literature survey
2.1. Time domain audio features in heavy vehicles
2.2. Frequency domain audio features
3. Comparison of Morlet, Mexican hat, frequency B-spline wavelets in classification of vehicle sound
3.1. Mexican hat wavelet transform
3.2. Morlet wavelet transform
3.3. Frequency B-spline wavelet transform
4. Conclusion
References
Chapter 11: Congestion control mechanisms in vehicular networks: A perspective on Internet of vehicles (IoV)
1. DCC mechanisms
1.1. UBPFCC
1.2. D-FPAV
1.3. PULSAR
1.4. LIMERIC
1.5. D-NUM
1.6. NDNUM
1.7. FABRIC
1.8. DCCS
1.9. Speed-based distributed congestion control algorithm
1.10. DisTraC
1.11. Multistate active DCC mechanism
1.12. Transmit data rate control-based DCC mechanism
1.13. Unequal power issue and age of information
2. Centralized congestion control mechanisms
2.1. ML-CC
2.2. DBDC
2.3. BSAM
2.4. MFCAR
2.5. HRLB
2.6. MLR
2.7. PRE-VE
2.8. DGGR
2.9. DBDR
3. Conclusion
References
Chapter 12: Smart traffic light management system for heavy vehicles
1. Introduction
2. Different techniques of traffic management systems for heavy vehicles
2.1. Manual traffic control system
2.2. Fixed time control system
2.3. Fuzzy expert system (FES)
2.4. Artificial neural networks (ANNs)
2.5. Wireless sensor network (WSN)
2.6. Image-processing based technique
2.7. Hybrid technique
3. Literature review
4. Scope of study
5. Proposed methodology
6. Results and discussion
7. Conclusion and future scope
References
Chapter 13: Smart automated system for classification of emergency heavy vehicles and traffic light controlling
1. Introduction
2. Literature survey
3. Methodology
3.1. Controlling of traffic light according to the real-time traffic density on the road
3.2. Emergency vs nonemergency vehicle classification
4. Design and implementation
4.1. Objective 1
5. Results and findings
5.1. Background subtraction method
5.1.1. Algorithm
5.2. Convolutional neural network
5.2.1. Convolution layer
5.2.2. ReLu layer
5.2.3. Pooling layer
6. Conclusion
References
Chapter 14: Implementation of a cooperative intelligent transport system utilizing weather and road observation data
1. Introduction
2. Related work
3. C-ITS communication and protocol
3.1. C-ITS components
3.2. C-ITS communication
3.3. C-ITS protocol
4. European framework of C-ITS
5. Validation framework and deployment of C-ITS pilot system
5.1. Validation framework for pilot system
5.2. Deployment of C-ITS pilot system
6. Results and discussion
7. Summary/conclusion
Acknowledgment
References
Section 3: Applications and case studies for heavy vehicles technologies
Chapter 15: Heavy vehicle defense procurement use cases and system design using blockchain technology
1. Introduction
1.1. Role of IT technology in defense
1.2. Defense deal and trading issues
1.3. Chapter key contributions
2. Blockchain technology in defense
2.1. Related work
2.2. Blockchain and defense system characteristics
2.3. Blockchain technology in defense applications
3. Use cases of defense blockchain
3.1. Supply chain management services in defense procurements
3.2. Data communication between defense forces
3.2.1. Ammunition management
3.2.2. Maintenance and renewal of service contracts using smart contracts
3.2.3. Heavy vehicle procurement using blockchain
4. Conclusion and future scope
Acknowledgments
References
Chapter 16: Cybercriminal approaches in big data models for automated heavy vehicles
1. Introduction
1.1. Automated heavy vehicle working trends
1.2. Wireless networks in automated heavy vehicles
1.3. Big data models
1.4. Cyberattacks in big data models
1.5. Organization of chapter
2. Cybersecurity and cyberattacks in networks (wired and wireless) for automated heavy vehicle movements
2.1. Types of cyberattacks
2.1.1. Malicious domains
2.1.2. Malware
2.1.3. Ransomware
2.2. Popular data breaches and cyberattacks
2.2.1. Malware-based fakes and spam
2.2.2. Email-based attacks
2.2.3. Fake mobile applications
2.2.4. UPI frauds
2.2.5. Twitter hack
2.2.6. Marriott data breach
2.2.7. Zoom credentials up for sale
2.2.8. Magellan health (ransomware attack and data breach)
2.3. Cyberattacks in automated heavy vehicle infrastructure
3. Data security measures for big data
3.1. Manage social media profile security in semi-automatic vehicle big data
3.2. Check privacy and security settings in heavy vehicle big data
4. Big data analytics for heavy autonomous vehicles
4.1. Big data-driven models for automated heavy vehicles
4.2. Security in big data-driven dynamic driving cycle development for electric buses
4.3. A big data-driven dynamic model for heavy trucks
4.4. High-resolution air pollution mapping with Google street view cars
4.5. Big data for internet of heavy vehicles
4.6. AI-based big data-driven models for automated heavy vehicles
4.7. Big data analytics for internet of heavy vehicles
5. Conclusion and future scope
References
Chapter 17: Modeling fuel economy of connected vehicles using driving context
1. Introduction
2. Literature review
2.1. Comparative analysis of different approaches
2.2. Limitation of existing approaches
3. Proposed architecture for estimating fuel efficiency
3.1. Problem formulation
3.2. The architecture of proposed solution
3.3. Design of factors affecting fuel economy-Defining predictor variables
3.4. Environmental context
3.5. Driving behavior identification
3.6. Model for prediction of fuel consumption
3.7. Application of GLM model for prediction of fuel consumption
3.8. Dataset
3.9. Framing GLM-based model for fuel consumption prediction
4. Results and discussion
4.1. Defining metrics for evaluating the accuracy of the model
4.2. Error distribution for SGLM
4.3. Error distribution of CGLM-L5
4.4. Error distribution of CGLM-L10
4.5. Error distribution of CGLM-L25
4.6. Comparison of SGLM, CGLM, and VolScore models
4.7. Comparison of predicted, calibrated, and observed values
5. Conclusion
References
Chapter 18: Conceptual design and computational investigations of fixed wing unmanned aerial vehicle for medium-range app
1. Introduction
2. Literature survey
3. Symbols
4. Conceptual design
4.1. Estimating wing surface area, wingspan, chord length, and fuselage length
4.2. Empennage design-Horizontal tail
4.3. Empennage and landing gear design-Stabiligear
4.4. Estimation of propulsive system and its weight
4.5. Estimation of co-efficient of lift for propeller
4.6. Estimation of co-efficient of lift for wing
4.7. Mechanical power estimation
4.8. Estimation of propellers pitch
4.9. Estimation of pitch angle and chord of the propeller
4.10. Aerofoil selection
4.11. Estimation of electrical and electronics system and its weight
4.12. Hybrid navigation system for medium range fixed wing UAVs
5. Conclusions
References
Chapter 19: Multi-sensor fusion in autonomous heavy vehicles
1. Introduction
1.1. Current status
1.2. Pros and cons of automated driving systems
2. Autonomous heavy vehicle subsystems
3. Communication protocols in autonomous heavy vehicles
4. ECU in autonomous heavy vehicles
5. The sensors used in autonomous heavy vehicles
6. Essential sensors used in ADSs
7. Sensor fusion in autonomous heavy vehicles
7.1. Levels of sensor fusion
7.1.1. Physical level fusion
7.1.2. Logical level
7.2. Working of sensor fusion module
8. Multi-sensor data fusion approaches
9. Advantages and challenges in multi-sensor data fusion in AHVs
10. Conclusion
11. Future directions
References
Chapter 20: Smart vehicle accident detection for flash floods
1. Introduction
1.1. Motivation of this research work
1.2. Modern technologies used for accident detection
1.3. Objectives of this research work
1.4. Chapter organization
2. Literature review
3. Proposed methodology
3.1. User registration and installation
3.2. Getting sensor data
3.3. Thresholding
3.4. Notification generation and fault-tolerant infrastructure
3.5. Response to the notification
3.6. Growing dataset and prediction models
3.7. Summary of the total accident detection procedure
4. Design and architecture
4.1. Server and app connectivity
4.2. SOS signal generation
4.3. SOS response and VANET infrastructure
4.4. Response from server
4.5. Impact of disasters on parameter threshold
5. System implementation
5.1. App installation and registration
5.2. App activation and user interface
5.2.1. Real time data and SOS numbers
5.2.2. History and secure me
5.3. SOS generation and confirmation
6. Result
6.1. SOS check and finding nearby hospitals
6.2. Fault tolerance of the system
6.3. Server compatibility in predicting disasters and accidents
7. Discussion
8. Conclusion and future directions
References
Further reading
Index
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