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.

• Introduction
• Part I: Control of CAVs
• Chapter 1: Human-vehicle shared control for highly automated vehicles
• Chapter 2: Mesoscopic control of traffic with mixed autonomy: sequencing, platooning, and routing
• Chapter 3: Dissipative barrier feedback for collision avoidance in vehicle platooning
• Chapter 4: Privacy-conscious, data-enabled predictive leading cruise control via affine masking
• Part II: Learning of CAVs
• Chapter 5: Highway platoon merging control using RL: a review
• Chapter 6: Advances in motion prediction and planning for autonomous vehicles: from classical methods to modern AI-based approaches
• Chapter 7: Data-driven predictive cruise control and cooperative adaptive cruise control for connected and autonomous vehicles based on reinforcement learning
• Chapter 8: Cyber-resilient learning-based controller design for adaptive cruise control
• Part III: Optimization of CAVs
• Chapter 9: Hierarchical framework of network-level routing and trajectory planning for emerging mobility systems
• Chapter 10: Safe interactions between autonomous and human-driven vehicles with cooperation compliance for social optimality
• Chapter 11: Real-time energy optimization approaches for connected and automated hybrid electric vehicles
• Chapter 12: Stochastic energy management strategies for connected hybrid electric vehicles