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.
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
Weinan Gao is a professor at Northeastern University, China. He received his PhD from New York University and previously held positions at Florida Tech, Georgia Southern, and MERL. His research focuses on reinforcement learning, adaptive optimal control, and intelligent transportation systems. He is an associate editor of IEEE TNNLS, IEEE/CAA JAS and Control Engineering Practice. He is the recipient of the best paper award in IEEE DDCLS, ICCAIS and RCAR.
Zhong-Ping Jiang is an institute professor at the Tandon School of Engineering, New York University, USA. He received the MSc. degree from the University of Paris XI, France, in 1989, and the PhD from the ParisTech-Mines, France, in 1993. His research interests include stability theory, constructive nonlinear control, learning-based control with applications to information, mechanical, biological and transportation systems. He is a member of the Academia Europaea and the European Academy of Sciences and Arts.
Andreas A. Malikopoulos is a professor at Cornell University, USA. He received a Diploma from the National Technical University of Athens, Greece, and his MS and PhD degrees from the University of Michigan. His research interests are grounded at the intersection of learning and control to enable systems to operate autonomously. His work integrates decision-theoretic foundations with learning-based methods to endow engineered systems with the capability to reason, learn, and act in real time.
