Radar Point Density Beats Architecture in Classification
π‘A simple data-density change nearly doubled macro F1, while architecture tweaks did not move the needle.
β‘ 30-Second TL;DR
What Changed
Macro F1 increased from 0.381 to 0.764 when point density rose from one to five points per instance.
Why It Matters
The result suggests that collecting denser radar observations may deliver more value than increasing model complexity in radar-only perception systems. Teams should measure the information available per object before investing in architecture search or feature engineering.
What To Do Next
Before tuning a radar classifier, bucket validation examples by points per instance and set a minimum-density gate for classes that cannot be separated reliably when sparse.
Key Points
- β’Macro F1 increased from 0.381 to 0.764 when point density rose from one to five points per instance.
- β’Large-vehicle F1 improved from 0.037 with one point to 0.995 with 11 or more points.
- β’Wider networks, deeper networks, alternative encodings, and different histogram bins produced no meaningful gains beyond the noise floor.
- β’Sparse radar observations caused stationary two-wheelers and pedestrians to become difficult to distinguish from compensated velocity alone.
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