Ensuring the safety of systems like truck platoons remains a significant challenge, especially when formal models of system behavior are unavailable or difficult to construct. In this work-in-progress, we explore an approach that uses automata learning to infer models from observed system executions, which can then be analyzed through model checking. The goal of this approach is to enable safety analysis without relying on manually specified models. We are investigating the feasibility of this idea through a Truck Platooning case study-an increasingly relevant scenario in intelligent transportation systems where safety is critical. While this approach is still under development, early steps suggest potential for combining learning-based modeling with formal verification to support safety analysis in both simulated and physical settings.