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.
Abstract (translated)
在基于摄像头的自动驾驶技术快速发展的背景下，当前研究往往侧重效果而忽视计算效率。针对这一问题，本文提出LRHPerception——一种用于自动驾驶的实时单目感知方案，通过单目相机视频解析周围环境。该系统融合了端到端学习的计算效率与局部建图方法的丰富表征细节，将目标跟踪预测、道路分割与深度估计的显著改进整合至统一框架，将单目图像数据处理为包含RGB、道路分割及像素级深度估计的五通道张量，并增强目标检测与轨迹预测功能。实验结果表明该系统性能强劲，在单GPU上实现29 FPS的实时处理速度，相较于最快的建图类方法提速555%。
URL
https://arxiv.org/abs/2603.21061
