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arXiv · 2603.21061

Single-Eye View: Monocular Real-time Perception Package for Autonomous Driving

Haixi Zhang·Aiyinsi Zuo·Zirui Li·Chunshu Wu·Tong Geng·Zhiyao Duan
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Abstract

Amidst the rapid advancement of camera-based autonomous driving technology, effectiveness is often prioritized with limited attention to computational efficiency. To address this issue, this paper introduces LRHPerception, a real-time monocular perception package for autonomous driving that uses single-view camera video to interpret the surrounding environment. The proposed system combines the computational efficiency of end-to-end learning with the rich representational detail of local mapping methodologies. With significant improvements in object tracking and prediction, road segmentation, and depth estimation integrated into a unified framework, LRHPerception processes monocular image data into a five-channel tensor consisting of RGB, road segmentation, and pixel-level depth estimation, augmented with object detection and trajectory prediction. Experimental results demonstrate strong performance, achieving real-time processing at 29 FPS on a single GPU, representing a 555% speedup over the fastest mapping-based approach.

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Haixi Zhang, Aiyinsi Zuo, Zirui Li, Chunshu Wu, Tong Geng, Zhiyao Duan. 2026-03-22. Single-Eye View: Monocular Real-time Perception Package for Autonomous Driving. https://arxiv.org/abs/2603.21061

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