基于基础设施的单目3D车辆定位框架及实验验证

中文摘要 AI 辅助

本文提出了一种单阶段学习框架，该框架将单目路边摄像头图像直接映射到地面固定坐标系中的车辆状态。与先在图像平面中检测车辆、随后应用几何后处理的传统方法不同，所提出的方法利用预训练目标检测器的特征，联合估计每辆车辆的地面平面位置、尺寸和偏航角。因此，该框架不仅将视觉特征用于车辆检测，还直接用于空间和姿态估计。为支持模型训练和评估，我们开发了一种基于路边摄像头和无人机（UAV）同步视频的数据采集与标签生成流程。无人机作为临时俯视传感平台，提供车辆轨迹、尺寸和方向，这些数据被转换到地面固定坐标系，并与路边摄像头图像进行时间对齐，以生成地面真值标签。该框架使用在Mcity试验场多次实验中收集的数据进行评估。结果表明，所提出的方法无需单独的几何后处理阶段，即可从单目路边图像中恢复车辆轨迹和方向，展示了其作为城市交叉路口基于基础设施感知的可扩展方法的潜力。

英文摘要

This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame. Unlike conventional approaches that first detect vehicles in the image plane and subsequently apply geometric post-processing, the proposed method leverages features from a pretrained object detector to jointly estimate each vehicle's ground-plane position, dimensions, and yaw angle. The framework therefore uses visual features not only for vehicle detection but also for direct spatial and orientation estimation. To support model training and evaluation, we develop a data-collection and label-generation pipeline based on synchronized video from a roadside camera and an unmanned aerial vehicle (UAV). Acting as a temporary top-view sensing platform, the UAV provides vehicle trajectories, dimensions, and orientations, which are transformed into the ground-fixed coordinate frame and temporally aligned with the roadside-camera images to generate ground-truth labels. The framework is evaluated using data collected during multiple experiments at the Mcity Test Facility. Results show that the proposed method can recover vehicle trajectories and orientations from monocular roadside imagery without a separate geometric post-processing stage, demonstrating its potential as a scalable approach to infrastructure-based perception at urban intersections.

发表机构

• Budapest University of Technology and Economics（布达佩斯技术与经济大学）
• University of Michigan（密歇根大学）

机构由 AI 辅助整理，请以论文原文为准。