Engineer's 3-layer MLP classifies automotive radar targets, Macro F1 hits 0.764
bruno_pinto90 · reddit · 2026-09-07
A radar signal processing engineer shared a full writeup of training a 5-class classifier (car/largevehicle/twowheeler/pedestrian/pedestriangroup) on RadarScenes point clouds.
- Method: per-scan 16-bin histogram input into a 3-layer MLP with class-weighted cross-entropy, based on the "Histogram-based Deep Learning for Automotive Radar" paper, single scan only.
- Data challenges: class imbalance for twowheelers and largevehicles, aggregated class semantics, and sequence bias from long slow tracks causing high F1 variance across folds.
- Ablations: bigger MLPs, alternative encodings, and different binning all mattered less than the train/val/test split itself (measured over 6 folds); per-instance statistics slightly degraded performance.
- Key finding: Macro F1 rises from 0.381 to 0.764 as detections per instance grow from 1 to 5 — sparsity is the bottleneck. Cars and pedestrians work best; twowheelers are worst, since velocity-compensated distributions overlap with pedestrians and a stationary two-wheeler is indistinguishable from one.
- Next steps: PointNet-style spatial encodings, multi-scan accumulation, and micro-Doppler.
Full report on GitHub.
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