3D HIGHWAY CURVE RECONSTRUCTION FROM MOBILE LASER SCANNING POINT CLOUDS THROUGH DEEP REINFORCEMENT LEARNING

Abstract. Reconstructing the geometric curves of highways holds significant value for various tasks, such as highway design, traffic simulation, and road network planning. An essential step in highway curve reconstruction is fitting highway curve models to the extracted road markings from laser scanning point clouds. Existing methods for highway curve fitting typically handle one curve at a time. However, a stretch of highway often contains multiple curves. In this paper, we introduce a novel method capable of fitting multiple highway curves simultaneously. The method leverages a reinforcement learning (RL) algorithm to achieve this goal. Specifically, we design a unique RL environment that empowers the RL algorithm to fit multiple highway curves. Our experimental results demonstrate the superiority of our method over other methods.