Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation

• URL: http://arxiv.org/abs/2508.04033v1
• Date: Wed, 06 Aug 2025 02:54:39 GMT
• Title: Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation
• Authors: Hee-Yeun Kim, Byeonggyu Park, Byonghyok Choi, Hansang Cho, Byungkwan Kim, Soomok Lee, Mingu Jeon, Seung-Woo Seo, Seong-Woo Kim,
• Abstract summary: Non-Line-of-Sight blind spots resulting from roadside parking pose a significant challenge to road safety.<n>mmWave technology leverages diffraction and reflection to observe NLoS regions.<n>We propose an NLoS pedestrian localization framework that integrates monocular camera image with 2D radar point cloud data.
• Score: 14.486658289318408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The presence of Non-Line-of-Sight (NLoS) blind spots resulting from roadside parking in urban environments poses a significant challenge to road safety, particularly due to the sudden emergence of pedestrians. mmWave technology leverages diffraction and reflection to observe NLoS regions, and recent studies have demonstrated its potential for detecting obscured objects. However, existing approaches predominantly rely on predefined spatial information or assume simple wall reflections, thereby limiting their generalizability and practical applicability. A particular challenge arises in scenarios where pedestrians suddenly appear from between parked vehicles, as these parked vehicles act as temporary spatial obstructions. Furthermore, since parked vehicles are dynamic and may relocate over time, spatial information obtained from satellite maps or other predefined sources may not accurately reflect real-time road conditions, leading to erroneous sensor interpretations. To address this limitation, we propose an NLoS pedestrian localization framework that integrates monocular camera image with 2D radar point cloud (PCD) data. The proposed method initially detects parked vehicles through image segmentation, estimates depth to infer approximate spatial characteristics, and subsequently refines this information using 2D radar PCD to achieve precise spatial inference. Experimental evaluations conducted in real-world urban road environments demonstrate that the proposed approach enhances early pedestrian detection and contributes to improved road safety. Supplementary materials are available at https://hiyeun.github.io/NLoS/.

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