Autonomous navigation in mobile robots has made significant advancements; however, traditional methods often struggle to adapt in real-time to dynamic or unstructured environments. This paper presents the Online Planner's Parameter Adaptation (OPPA) framework, which enhances both adaptability and safety in mobile robot navigation by dynamically adjusting planner parameters. OPPA integrates a rule-based system for estimating tunnel width using 2D LiDAR and path data with a learning-based approach utilizing a shallow transformer model. By incorporating a human-in-the-loop process to refine training data, OPPA improves accuracy and reliability in complex environments. Designed for real-time efficiency on resource-constrained platforms, OPPA has been validated through simulation and real-world experiments, demonstrating its ability to enhance both safety and performance. These results highlight OPPA as a viable solution for dynamic and complex robotic applications.