Connected and autonomous vehicles (CAVs) have enormous potential to shape the future of transportation. As this complex and dynamic field grows, researchers are looking for ways to improve the efficiency and performance of CAVs. Through employing predictive modeling, machine learning, and advanced sensor fusion approaches, CAVs can anticipate and respond to hazardous situations with greater precision and speed. Control algorithms coupled with real-time data analysis enable CAVs to achieve significant reductions in energy consumption without compromising performance or safety.
This book investigates the convergence of control, learning, and optimization techniques used to enhance CAV safety, mobility, energy efficiency, and overall performance, helping readers gain a deeper understanding of the key developments and emerging trends in CAV technologies.
It includes chapters on human-vehicle shared control, vehicle platooning, motion prediction and planning for autonomous vehicles, predictive and adaptive cruise control, reinforcement learning, energy optimisation, as well as cyber-security and privacy issues in learning-based vehicle control.
This book is a comprehensive resource for researchers and advanced students interested in the transformative potential of CAVs in future transport and looking for further insights to navigate this complex and dynamic field.