With its well-thought-out overview of the topic, Embedded Systems in Automotive Applications serves diverse audience across both in academia and industry. The volume discusses the AUTOSAR architecture and standards, highlighting their role in enabling the integration of emerging technologies such as blockchain-based security within the Internet of Vehicles. It further addresses embedded software design, implementation, and validation techniques that are fundamental to developing reliable and safety-critical automotive systems. In addition, it examines electric vehicle technologies, with particular focus on advanced battery management strategies and real-time power and health monitoring. Autonomous driving is explored through LiDAR-SLAM techniques and FPGA-based hardware architectures designed to support robust vehicle navigation. The book also emphasizes vehicle connectivity and telematics applications, encompassing innovations in smart mobility and intelligent transport. Together, these themes provide readers with a rigorous and well-rounded understanding of the challenges, methodologies, and opportunities shaping next-generation automotive embedded systems.
1. I: Introduction - p. P1 2. Chapter 1. Overview of embedded systems in automotive applications - p. 3 3. II: AUTOSAR Architecture & Standards - p. P27 4. Chapter 2. AUTOSAR in automotive embedded systems: classic versus adaptive platforms and their role in IoV development with integrated blockchain security - p. 29 5. Chapter 3. Multithreading integration in automotive computing: AUTOSAR-compliant survey - p. 63 6. Chapter 4. A concise survey on ADAS: advances in vehicle and pedestrian detection with a focus on the AUTOSAR automotive standard - p. 77 7. III: Embedded software implementation & validation - p. P107 8. Chapter 5. Efficient parallel implementation strategies on multicore digital signal processors - p. 109 9. Chapter 6. Google Test/Google Mock to verify critical embedded software - p. 121 10. Chapter 7. MIL and PIL tests for SOC estimation algorithms of electric vehicles lithium-ion batteries and their implementation on ARM embedded processor - p. 139 11. IV: Battery Management Systems & Monitoring - p. P159 12. Chapter 8. Battery Management Systems: integration and optimization in automotive applications - p. 161 13. Chapter 9. Battery real-time power monitoring and control in automotive embedded systems - p. 217 14. V: Autonomous Driving Technologies - p. P255 15. Chapter 10. A comprehensive review of LiDAR-SLAM for hardware implementation for autonomous vehicles - p. 257 16. Chapter 11. Efficient FPGA-based hardware architecture for high-performance EKF-SLAM in autonomous vehicle navigation - p. 285 17. Chapter 12. Advancing autonomous vehicle technology with 3D LiDAR and SqueezeSeg-ROS integration - p. 307 18. VI: Connectivity & Telematics - p. P329 19. Chapter 13. 3G Universal plugin for car remote starters, alarm systems, and car key fobs - p. 331 20. Index - p. 355
With its well-thought-out overview of the topic, Embedded Systems in Automotive Applications serves diverse audience across both in academia and industry. The volume discusses the AUTOSAR architecture and standards, highlighting their role in enabling the integration of emerging technologies such as blockchain-based security within the Internet of Vehicles. It further addresses embedded software design, implementation, and validation techniques that are fundamental to developing reliable and safety-critical automotive systems. In addition, it examines electric vehicle technologies, with particular focus on advanced battery management strategies and real-time power and health monitoring. Autonomous driving is explored through LiDAR-SLAM techniques and FPGA-based hardware architectures designed to support robust vehicle navigation. The book also emphasizes vehicle connectivity and telematics applications, encompassing innovations in smart mobility and intelligent transport. Together, these themes provide readers with a rigorous and well-rounded understanding of the challenges, methodologies, and opportunities shaping next-generation automotive embedded systems.
1. I: Introduction - p. P1 2. Chapter 1. Overview of embedded systems in automotive applications - p. 3 3. II: AUTOSAR Architecture & Standards - p. P27 4. Chapter 2. AUTOSAR in automotive embedded systems: classic versus adaptive platforms and their role in IoV development with integrated blockchain security - p. 29 5. Chapter 3. Multithreading integration in automotive computing: AUTOSAR-compliant survey - p. 63 6. Chapter 4. A concise survey on ADAS: advances in vehicle and pedestrian detection with a focus on the AUTOSAR automotive standard - p. 77 7. III: Embedded software implementation & validation - p. P107 8. Chapter 5. Efficient parallel implementation strategies on multicore digital signal processors - p. 109 9. Chapter 6. Google Test/Google Mock to verify critical embedded software - p. 121 10. Chapter 7. MIL and PIL tests for SOC estimation algorithms of electric vehicles lithium-ion batteries and their implementation on ARM embedded processor - p. 139 11. IV: Battery Management Systems & Monitoring - p. P159 12. Chapter 8. Battery Management Systems: integration and optimization in automotive applications - p. 161 13. Chapter 9. Battery real-time power monitoring and control in automotive embedded systems - p. 217 14. V: Autonomous Driving Technologies - p. P255 15. Chapter 10. A comprehensive review of LiDAR-SLAM for hardware implementation for autonomous vehicles - p. 257 16. Chapter 11. Efficient FPGA-based hardware architecture for high-performance EKF-SLAM in autonomous vehicle navigation - p. 285 17. Chapter 12. Advancing autonomous vehicle technology with 3D LiDAR and SqueezeSeg-ROS integration - p. 307 18. VI: Connectivity & Telematics - p. P329 19. Chapter 13. 3G Universal plugin for car remote starters, alarm systems, and car key fobs - p. 331 20. Index - p. 355
