Autonomous Driving and Mixed Traffic Dynamics: Modeling, Simulation, and Control provides an introduction to autonomous vehicles (AV) in road transport systems, along with discussions on the critical similarities and differences with human drivers. Focusing on key concepts in traffic dynamics and AI-based modeling, the book also offers a comprehensive discussion of the unique dynamics introduced by AVs and their impacts on congestion, safety, energy, and their role in sustainable future intelligent transportation systems. Sections delve into traditional driving behaviors, examining the basics of car-following, driver characteristics, lateral movement, and how well AI models generalize these behaviors. Further sections covers shifts to autonomous driving systems, analyzing their operational principles and providing comparative evidence with human drivers. Assessments on the performance of traditional car-following models against artificial intelligence developments that highlight strengths and weaknesses for each approach are also included. Final sections integrate human drivers and AVs into broader traffic flow theories, presenting findings on how autonomous driving impacts traffic patterns, look at the role of AI and modeling, explore the pros and cons of various methods and data sources, and discuss real-world traffic management applications, combining AI and traditional models for traffic estimation, control, and ensuring fair, disruption-resilient outcomes. Authored by a team with years of expertise and cross-disciplinary interaction in Computer Science, Mechanical Engineering, and Traffic Engineering Utilizes a step-by-step approach to exploring the implications of Autonomous Vehicles, beginning with foundational concepts and progressively extending to their impact on segment-level traffic dynamics, operations, and broader network level Provides definitions of key terms, methods, applications, case studies, reviews, the latest research, and future implications