Control, Learning, and Optimization with Applications in Connected and Autonomous 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, and 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.

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