Situational awareness (SA) is crucial in convoy-following tasks, where drivers must accurately anticipate and respond to the lead vehicle’s behavior to ensure safety and efficiency. This study investigates the role of mental models (MMs) in enhancing SA, focusing on the design of human-machine interfaces (HMI) that align with drivers’ cognitive mechanisms. The research explores how various HMI designs, guided by key mental models, improve drivers’ ability to perceive, understand, and predict the lead vehicle’s behavior in dynamic driving scenarios. Four fundamental MMs are identified: the distance perception model, the response prediction model, the risk assessment model, and the attention allocation model. The study evaluates HMI interface designs that vary in the level and type of information displayed to the driver, with a focus on visualizations of lead vehicle status and following distances. The results indicate that presenting multiple levels of information significantly enhances SA by providing drivers with contextually relevant data, without overwhelming their cognitive resources. These findings offer valuable insights into designing adaptive HMI systems that support drivers in complex, high-pressure environments, improving situational awareness and overall performance in convoy-following tasks.