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Handbook of power electronics in autonomous and electric vehicles /

The 'Handbook of Power Electronics in Autonomous and Electric Vehicles', edited by Muhammad H. Rashid, provides a comprehensive exploration of the role of power electronics in the development and operation of autonomous and electric vehicles. It covers various aspects, including the histor...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Rashid, Muhammad H. (Muhammad Harunur), 1945-
Format: eBook
Language:English
Published: London : Academic Press, 2024.
Subjects:
Power electronics > Handbooks, manuals, etc.
Electric vehicles > Handbooks, manuals, etc.
Automated vehicles > Handbooks, manuals, etc.
Électronique de puissance > Guides, manuels, etc.
Véhicules électriques > Guides, manuels, etc.
Véhicules autonomes > Guides, manuels, etc.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Handbook of Power Electronics in Autonomous and Electric Vehicles
  • Copyright
  • Contents
  • Contributors
  • Chapter 1: Introduction to autonomous vehicles
  • 1.1. Introduction
  • 1.1.1. Motivation
  • 1.1.2. History of autonomous vehicles
  • 1.1.3. State of the industry
  • 1.2. Impacts of autonomous vehicles
  • 1.3. Autonomous vehicles: Review of control methods
  • 1.3.1. Using AI-based technologies in AVs
  • 1.4. Energy efficiency in autonomous vehicles
  • 1.5. Challenges
  • 1.5.1. Safety and security challenges
  • 1.5.2. Testing and verification challenges
  • 1.5.3. Other challenges
  • 1.5.3.1. Production and cost
  • 1.5.3.2. Technological limitations
  • 1.5.3.3. Electrification
  • 1.6. Summary and conclusion
  • Acknowledgment
  • References
  • Chapter 2: Introduction to autonomous systems
  • 2.1. Historical evolution of autonomous systems
  • 2.1.1. Autonomous systems
  • 2.1.1.1. Self-governing nature
  • 2.1.1.2. Internal algorithms
  • 2.1.1.3. Sensor inputs
  • 2.1.1.4. Decision-making
  • 2.1.1.5. Adaptation to changing circumstances
  • 2.1.1.6. Degree of autonomy
  • 2.1.2. Decision-making and action-taking
  • 2.1.3. Sensor-actuator synergy
  • 2.1.4. Harnessing artificial intelligence
  • 2.1.5. Examples of autonomous systems
  • 2.1.6. The purpose of autonomy
  • 2.2. Key components of autonomous systems
  • 2.2.1. Sensors
  • 2.2.2. Perception algorithms
  • 2.2.3. Decision-making algorithms
  • 2.2.4. Actuators
  • 2.3. Levels of autonomy
  • 2.4. Applications of autonomous systems
  • 2.5. Challenges and future directions
  • 2.5.1. Challenges of autonomous systems
  • 2.5.2. Future directions of autonomous systems
  • 2.6. Conclusion
  • References
  • Chapter 3: Sensors for autonomous vehicles
  • 3.1. Introduction
  • 3.2. Autonomous vehicles sensors and technologies
  • 3.2.1. Camera
  • 3.2.2. The pinhole model.
  • 3.2.3. 2D cameras and 3D cameras
  • 3.2.3.1. Stereo camera
  • 3.2.3.2. Time-of-flight
  • 3.2.3.3. Structured light
  • 3.2.3.4. Laser triangulation
  • 3.2.4. Visible camera sensors and infrared camera sensors
  • 3.2.5. CCD camera sensors and CMOS camera sensors
  • 3.2.6. Lidar
  • 3.2.7. Measuring principle
  • 3.2.7.1. Time-of-flight lidar
  • 3.2.7.2. AMCW lidar
  • 3.2.7.3. FMCW lidar
  • 3.2.8. Lidar operating at 905 and 1550nm
  • 3.2.9. Beam deflection mechanism
  • 3.2.9.1. Mechanical lidar
  • 3.2.9.2. Solid state lidar
  • 3.2.10. Data representation
  • 3.2.11. Radar
  • 3.2.12. Ultrasonic sensor
  • 3.2.13. GPS and IMU
  • 3.3. Sensor fusion
  • 3.3.1. Data fusion
  • 3.3.2. Calibration
  • 3.3.3. Synchronization
  • 3.4. Still challenging
  • 3.4.1. Weather/road conditions
  • 3.4.2. Security
  • 3.4.3. Computation
  • References
  • Chapter 4: Radar architectures and cyberattacks from an autonomous vehicles perspective
  • 4.1. Early history of electromagnetism and radio waves
  • 4.2. Electromagnetic spectrum
  • 4.3. Classification of radars
  • 4.4. Radar range equation
  • 4.5. Pulsed radar
  • 4.6. Doppler radar
  • 4.7. FMCW radar
  • 4.8. OFDM radar
  • 4.9. Other types of radars
  • 4.9.1. Synthetic aperture radar
  • 4.9.2. Ground penetrating radar
  • 4.9.3. UWB see-through-wall radars
  • 4.9.4. Software-defined radar
  • 4.10. Radar jamming and cyberattacks
  • 4.11. Conclusion
  • Acknowledgment
  • References
  • Chapter 5: Artificial intelligence for autonomous vehicles: Comprehensive outlook
  • 5.1. Introduction to the understudy subject
  • 5.2. AI and transportation
  • 5.3. Research background and recent work of the subject
  • 5.4. Definition of AVs
  • 5.5. Role of AI in AVs
  • 5.6. Architectures for self-driving cars based on deep faded learning
  • 5.6.1. Deep CNNs
  • 5.6.2. Recurrent neural networks
  • 5.6.3. Deep reinforcement learning
  • 5.7. Sensor fusion.
  • 5.7.1. What is the sensor fusion?
  • 5.7.2. How does sensor fusion work?
  • 5.7.3. Theory behind sensor fusion
  • 5.7.4. Importance of sensor fusion
  • 5.7.5. Different types of sensors used in AVs
  • 5.7.6. Steps involved in sensor fusion
  • 5.8. Perception and localization
  • 5.8.1. What is the definition of perception and localization?
  • 5.8.2. How do these two work?
  • 5.8.3. The theory behind perception and localization
  • 5.8.4. Object detection
  • 5.8.5. Classification and recognition
  • 5.9. Mapping
  • 5.9.1. What does mapping mean?
  • 5.9.2. How does it work?
  • 5.9.3. Theory behind the mapping
  • 5.10. Decision-making and control
  • 5.10.1. How does it work?
  • 5.10.2. The theory behind the decision-making approach
  • 5.11. Prediction of driving condition
  • 5.11.1. Path planning
  • 5.11.2. Controlling of motion
  • 5.12. Advantages of AVs
  • 5.13. Challenges to overcome
  • 5.14. AI and ML in autonomous vehicles
  • 5.15. The future landscape
  • 5.16. Summary and conclusion
  • References
  • Chapter 6: Security for autonomous vehicles
  • 6.1. Introduction
  • 6.2. Different components in autonomous vehicles
  • 6.2.1. Sensing devices
  • 6.2.2. Control units
  • 6.2.3. Inner-vehicle network communications
  • 6.2.4. Vehicle to everything (V2X) technology
  • 6.3. Major cyber attacks for autonomous vehicles
  • 6.3.1. Manipulation-based attacks
  • 6.3.1.1. Man in the middle (MITM) attacks
  • 6.3.1.2. Injection attacks
  • 6.3.1.3. Tampering attacks
  • 6.3.2. Identity-based attacks
  • 6.3.2.1. Spoofing attack
  • 6.3.2.2. Replay attacks
  • 6.3.3. Functionality compromise incidents
  • 6.3.3.1. Denial of service (DoS) attacks
  • 6.3.3.2. Jamming attacks
  • 6.3.3.3. Sensor blinding attacks
  • 6.3.3.4. Spamming attacks
  • 6.3.4. Code manipulation intrusions
  • 6.3.4.1. Malware attacks
  • 6.3.4.2. Mobile app attacks.
  • 6.3.5. Physical access through the attacks
  • 6.3.5.1. Direct access attacks
  • 6.3.5.2. Indirect access attacks
  • 6.4. Defense strategies for cyber security in autonomous vehicles
  • 6.4.1. Defense for in-vehicle network attacks
  • 6.4.1.1. Against remote sensor attacks
  • 6.4.1.2. Against GPS spoofing
  • 6.4.1.3. Against CAN and SAE J1939 buses vulnerabilities
  • 6.4.1.4. Against ECUs software flashing attacks
  • 6.4.2. Defense for vehicle-to-everything network attacks
  • 6.4.2.1. Against DoS attacks
  • 6.4.2.2. Against impersonations attacks
  • 6.4.2.3. Against replay attacks
  • 6.4.2.4. Against data falsifications attacks
  • 6.4.3. Defense for against other attacks
  • 6.4.3.1. Against infrastructure attacks
  • References
  • Chapter 7: Security challenges facing autonomous and electric vehicles
  • 7.1. Introduction
  • 7.2. Motivation
  • 7.3. The state of insecurity
  • 7.4. Autonomous vehicle security concerns
  • 7.4.1. Adversarial attack vectors
  • 7.4.2. Sensor and system vulnerabilities
  • 7.4.3. Validating the readiness of autonomous vehicles
  • 7.4.4. Challenges facing AV deployment
  • 7.5. Electric vehicle charge station (EVCS) security
  • 7.6. Generalized mathematical model under attacks
  • 7.7. Case studies
  • 7.7.1. Vulnerability demonstrations
  • 7.7.1.1. Automatic emergency brake under false data injection attack
  • 7.7.1.2. Cooperative adaptive cruise control under FDI attack
  • 7.8. Summary and conclusion
  • Acknowledgment
  • References
  • Chapter 8: Intelligent energy management system for autonomous vehicles
  • 8.1. Introduction
  • 8.2. Components and subsystems
  • 8.3. Intelligent energy management system
  • 8.3.1. Vehicle tasks in intelligent energy management system
  • 8.3.2. Automation level in intelligent energy management system
  • 8.3.3. Sensors in intelligent energy management system.
  • 8.3.4. Algorithms and architectures in intelligent energy management system
  • 8.4. Recent studies and developments
  • 8.5. Mathematical model of an intelligent energy management system
  • 8.5.1. Q-learning algorithm
  • 8.5.2. Self-adaptive Q-learning algorithm
  • 8.5.3. Dynamic programming
  • 8.5.4. Reinforcement learning controller
  • 8.5.5. Q-learning controller algorithm
  • 8.5.6. Self-adaptive Q-learning controller
  • 8.5.7. Convergence and robustness of model
  • 8.6. Simulations and results
  • 8.7. Summary
  • References
  • Chapter 9: Hardware security of autonomous vehicles
  • 9.1. Hardware security
  • 9.1.1. What is hardware security issue?
  • 9.1.2. What are hardware trust issues?
  • 9.1.3. Hardware security models
  • 9.1.4. Hardware attacks
  • 9.1.4.1. Hardware Trojan-based attacks
  • 9.1.4.2. Side-channel attacks (SCA)
  • 9.1.4.3. IP theft attacks
  • 9.1.4.4. Counterfeit attacks
  • 9.1.4.5. Hardware attacks due to adversarial environment to the sensors
  • 9.1.5. Countermeasures against hardware attacks
  • 9.1.5.1. Hardware Trojan detection and mitigation techniques
  • 9.1.5.2. Side-channel mitigation techniques
  • 9.1.5.3. Physically unclonable functions (PUF)
  • 9.1.5.4. Component for hardware security
  • 9.2. Effect of hardware security on autonomous vehicle
  • 9.2.1. Hardware security for system architecture of the autonomous vehicle
  • 9.2.1.1. Perception layer
  • 9.2.1.2. Decision layer
  • 9.2.1.3. Control layer
  • 9.2.1.4. Actuators
  • 9.2.2. Hardware security for management modules in autonomous vehicles
  • 9.2.2.1. Communication network of autonomous vehicle
  • 9.2.2.2. Hardware security in AV's electrical power distribution system
  • 9.3. Roadmap of autonomous vehicles and associated security risks
  • References
  • Chapter 10: Propulsion drives and control algorithms of electrical vehicles
  • 10.1. Introduction.