This study investigates the complexities of Mixed Traffic with Fleets of Automated Vehicles (MTF-AVs) in underground mining environments characterized by confined spaces, limited visibility, and strict navigation requirements. The research focuses on integrating human-controlled vehicles into coordinated AV fleets, addressing the unpredictable interactions that arise from human behaviour. The ORU coordination framework, originally designed for a fully autonomous system, is adapted for mixed traffic scenarios to evaluate the impact of human behaviour on system efficiency and safety. Through a series of simulations, the study explores how fleet coordination algorithms adapt to human driver behaviour. These simulations demonstrate that human error and rule violations significantly reduce performance, increasing safety risks and decreasing efficiency. Findings emphasize the need for advanced coordination algorithms that dynamically adapt to unpredictable human behaviour in MTF-AVs. Such algorithms would optimize interactions between automated and human-controlled vehicles, enhancing both safety and efficiency in these complex and dynamic environments. Future research will further explore the influence of human behaviour on the coordination system and develop advanced coordination algorithms with methods to evaluate these interactions effectively.