This paper presents LRHPerception, a real-time monocular perception package designed specifically for autonomous driving applications. The system leverages monocular camera video to interpret the surrounding environment by combining the computational efficiency of end-to-end learning with detailed local mapping methodologies. It achieves comprehensive perception through integrated object tracking and prediction, road segmentation, and depth estimation capabilities. The unified framework processes monocular images into a five-channel tensor containing RGB, road segmentation, and pixel-level depth information, enhanced with object detection and trajectory prediction. Experimental results demonstrate impressive real-time performance at 29 FPS on a single GPU, representing a 555% speedup compared to baseline approaches.