Preprint
Nov 2026
Refining Roadside 4D Radar Vehicle Classification Using Point-Cloud Dispersion
A Neyman-Pearson-based binary classifier is proposed on the temporally-smoothed 2D positional standard deviation of radar point cloud clusters, reducing classification error from 52.1% to 42.7% and lays the foundation for future camera-radar fusion.
Mohammed Alsakabi, Andreia Figueiredo, António Almeida et al.
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