反射感知推理用于非视距行人定位
Reflection-Aware Reasoning for Non-Line-of-Sight Pedestrian Localization
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- Seoul National University（首尔国立大学）
- Pusan National University（釜山国立大学）
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中文总结 AI 辅助
针对自车动态室外环境中的非视距行人定位难题，提出融合相机与雷达点云、推断反射信息并利用物理引导光线追踪的框架，实验验证其有效性。
中文摘要 AI 辅助
非视距（NLOS）行人的可靠定位对于城市安全自动驾驶至关重要，然而在自车动态的室外环境中，这仍然极具挑战性，因为自车运动使得雷达多径传播变得复杂且嘈杂。本文提出了一种反射感知框架，用于在室外测试场景中实现移动自车下的非视距行人定位。该框架融合前视相机图像和二维雷达点云，以在鸟瞰视角空间中推断反射阶次和反射表面分布。随后，它利用物理引导的光线追踪来重建失真的反射路径并定位隐藏行人。我们在自车动态条件下的室外测试场景中验证了该框架。结果表明，所提出的框架对于移动自车下的非视距行人定位是有效的。
英文摘要
Reliable localization of non-line-of-sight (NLOS) pedestrians is critical for safe urban autonomous driving, yet it remains highly challenging in ego-dynamic outdoor environments, where ego-vehicle motion makes radar multipath propagation complex and noisy. In this paper, we present a reflection-aware framework for NLOS pedestrian localization with a moving ego-vehicle in outdoor testbed scenarios. Our framework fuses front-view camera images and 2D radar point clouds to infer reflection orders and reflective surface distributions in bird's-eye-view space. It then uses physics-guided ray tracing to reconstruct distorted reflection paths and localize the hidden pedestrian. We validate the framework in outdoor testbed scenarios under ego-dynamic conditions. The results demonstrate the effectiveness of the proposed framework for NLOS pedestrian localization with a moving ego-vehicle.
