Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems
eBook - ePub

Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems

About this book

This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors.

This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles.

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.

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Yes, you can access Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems by Meenakshi Malik,A.V. Senthil Kumar,Alptekin Erkollar,Piotr Maśloch in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Automotive Transportation & Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Series
  4. Title
  5. Copyright
  6. Dedication
  7. Contents
  8. Editor Biographies
  9. List of Contributors
  10. Foreword
  11. Preface
  12. Acknowledgments
  13. 1 Intelligent Transport Systems: An Introduction to Sensing and AI
  14. 2 Safety and Reliability in Autonomous Vehicles
  15. 3 Sensing Technologies for Autonomous Vehicles: LiDAR
  16. 4 AI-Driven Autonomous Vehicles: Radar-Based Perception
  17. 5 Reinforcement Learning–Based Vulnerability Identification in Intelligent Transport Systems
  18. 6 Sensor Data Processing and Interpretation
  19. 7 Enhancing Autonomous Vehicle Safety with a Control-Optimizer Framework Leveraging Smart Sensing and Actuation
  20. 8 Ensuring Safety and Reliability in Autonomous Vehicles: A Critical Analysis Using Artificial Intelligence
  21. 9 Intelligent Battery Management for EVs: A Machine Learning Approach to Predictive Maintenance and Energy Optimization
  22. 10 Challenges and Future Directions in Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems
  23. 11 AI-Driven Safety Enhancements in Autonomous Vehicles: Trends, Challenges, and Opportunities
  24. 12 Navigating Ethics and Practicality: AI-Driven Autonomous Vehicles in a Modern World
  25. 13 Future Trends and Innovations in AI-Driven Electric Vehicles Automation
  26. 14 Introduction to Camera Sensing Technologies and AI in Mobility
  27. Index