用摄像头提取道路布局，提升毫米波雷达在路口盲区的行人定位精度。
mmWave Radar-Based Non-Line-of-Sight Pedestrian Localization at T-Junctions Utilizing Road Layout Extraction via Camera
- 融合摄像头视觉信息解析雷达点云，重建盲区空间结构。
- 实车实验验证方法有效，可实现复杂城市路口的行人精确定位。
- 适合自动驾驶中解决非视距障碍物感知难题，尤其适用于路口场景。
城市环境中非视距（NLoS）区域的行人定位对自动驾驶系统构成重大挑战。虽然毫米波雷达在该场景下具有探测潜力，但其二维雷达点云数据易受多路径反射干扰，导致空间推理困难；而相机虽能提供高分辨率视觉信息，却缺乏深度感知，无法直接观测遮挡区域的行人。本文提出一种新框架，利用摄像头推断的道路布局来解释毫米波雷达的二维点云，实现对非视距行人的定位。该方法通过视觉信息解析雷达数据，支持空间场景重建。实验基于安装于真实车辆上的雷达-相机系统开展，使用在户外非视距驾驶环境采集的数据集进行评估，验证了该方法的实用性。
原文摘要 · Abstract (English)
Pedestrians Localization in Non-Line-of-Sight (NLoS) regions within urban environments poses a significant challenge for autonomous driving systems. While mmWave radar has demonstrated potential for detecting objects in such scenarios, the 2D radar point cloud (PCD) data is susceptible to distortions caused by multipath reflections, making accurate spatial inference difficult. Additionally, although camera images provide high-resolution visual information, they lack depth perception and cannot directly observe objects in NLoS regions. In this paper, we propose a novel framework that interprets radar PCD through road layout inferred from camera for localization of NLoS pedestrians. The proposed method leverages visual information from the camera to interpret 2D radar PCD, enabling spatial scene reconstruction. The effectiveness of the proposed approach is validated through experiments conducted using a radar-camera system mounted on a real vehicle. The localization performance is evaluated using a dataset collected in outdoor NLoS driving environments, demonstrating the practical applicability of the method.
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