Autonomous exploration has become a crucial technology for mobile robots, and numerous broadly applicable algorithms have emerged. However, few exploration methods effectively utilize the features of specified types of areas to enhance the efficiency of autonomous exploration in a complex environment. In this paper, we propose ACS-MM-Explore, an adaptive-circular-search-based exploration framework for large-scale urban road environments, focusing on extracting and utilizing the boundaries of roads to enhance exploration efficiency. Our approach integrates a multi-modal traversabil-ity analysis module to distinguish between road and non-traversable areas on a 2D costmap. A novel mechanism for gen-erating exploration viewpoints is introduced, efficiently creating exploration viewpoints with a circular search process with an adaptive radius. An optimized viewpoint selection mechanism is included, taking into account the geographical and geomet-ric information of each viewpoint. The framework extends the move base and TEB local planner as a viewpoint-based navigation module. A comprehensive evaluation is concluded in a simulation environment, demonstrating the framework's effectiveness and robustness.