Reflection-Aware Reasoning for Non-Line-of-Sight Pedestrian Localization

Reliable localization of non-line-of-sight (NLOS) pedestriansis critical for safe urban autonomous driving, yet it remains highly chal-lenging in ego-dynamic outdoor environments, where ego-vehicle motionmakes radar multipath propagation complex and noisy. In this paper, wepresent a reflection-aware framework for NLOS pedestrian localizationwith a moving ego-vehicle in outdoor testbed scenarios. Our frameworkfuses front-view camera images and 2D radar point clouds to infer reflec-tion orders and reflective surface distributions in bird’s-eye-view space.It then uses physics-guided ray tracing to reconstruct distorted reflec-tion paths and localize the hidden pedestrian. We validate the frame-work in outdoor testbed scenarios under ego-dynamic conditions. The re-sults demonstrate the effectiveness of the proposed framework for NLOSpedestrian localization with a moving ego-vehicle.