A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions
This addresses safety-critical perception for industrial and subterranean applications, offering a robust alternative to failing camera and LiDAR systems, though it is incremental as it adapts existing radar processing techniques.
The paper tackled the problem of reliable human detection in harsh industrial and underground environments where optical sensors fail, by developing a model-driven 4D radar framework that maintained stable pedestrian identification in dust-filled and confined spaces.
Pervasive sensing in industrial and underground environments is severely constrained by airborne dust, smoke, confined geometry, and metallic structures, which rapidly degrade optical and LiDAR based perception. Elevation resolved 4D mmWave radar offers strong resilience to such conditions, yet there remains a limited understanding of how to process its sparse and anisotropic point clouds for reliable human detection in enclosed, visibility degraded spaces. This paper presents a fully model-driven 4D radar perception framework designed for real-time execution on embedded edge hardware. The system uses radar as its sole perception modality and integrates domain aware multi threshold filtering, ego motion compensated temporal accumulation, KD tree Euclidean clustering with Doppler aware refinement, and a rule based 3D classifier. The framework is evaluated in a dust filled enclosed trailer and in real underground mining tunnels, and in the tested scenarios the radar based detector maintains stable pedestrian identification as camera and LiDAR modalities fail under severe visibility degradation. These results suggest that the proposed model-driven approach provides robust, interpretable, and computationally efficient perception for safety-critical applications in harsh industrial and subterranean environments.