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.
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 deep understanding of the key developments and emerging trends in CAV technologies.
- Chapter 1: Introduction
- Chapter 2: Human-Vehicle Shared Control for Highly Automated Vehicles
- Chapter 3: Mesoscopic Control of Traffic with Mixed Autonomy: Sequencing, Platooning, and Routing
- Chapter 4: Dissipative Barrier Feedback for Collision Avoidance in Vehicle Platooning
- Chapter 5: Privacy-Conscious Data-Enabled Predictive Leading Cruise Control via Affine Masking
- Chapter 6: Highway Platoon Merging Control using RL: A Review
- Chapter 7: Advances in Motion Prediction and Planning for Autonomous Vehicles: From Classical Methods to Modern AI-Based Approaches
- Chapter 8: Data-Driven Predictive Cruise Control and Cooperative Adaptive Cruise Control for Connected and Autonomous Vehicles based on Reinforcement Learning
- Chapter 9: Cyber-Resilient Learning-Based Controller Design for Adaptive Cruise Control
- Chapter 10: Hierarchical Framework of Network-Level Routing and Trajectory Planning for Emerging Mobility Systems
- Chapter 11: Safe Interactions Between Autonomous and Human-Driven Vehicles with Cooperation Compliance for Social Optimality
- Chapter 12: Real-time Energy Optimization Approaches for Connected and Automated Hybrid Electric Vehicle
- Chapter 13: Stochastic Energy Management Strategies for Connected Hybrid Electric Vehicles