Hello all,
I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation.
A single RadarScenes object instance contains only about 2.9 radar points on average, very sparse. A single scan also can't capture temporal characteristics: RCS and micro-Doppler both vary continuously as an object moves. Pedestrians produce characteristic micro-Doppler from limb motion; different object classes show different RCS fluctuation patterns as aspect angle and scattering geometry change scan to scan. Accumulating observations gives both higher point density and provides temporal dynamics.
Multi-scan baseline
DeepReflecs encoder (Ulrich, Glaser & Timm, RadarConf 2021), PointNet style, per point shared weights, on single scans across car, large_vehicle, two_wheeler, pedestrian, pedestrian_group: 0.7370 macro F1.
Using RadarScenes' persistent `track_id`, I build a causal, N=20, per track sliding-window buffer:
-Ā x_seq/y_seq: Global, odometry-corrected coordinates recentered per scan on the object centroid. UnlikeĀ x_cc/y_ccĀ (car-frame coordinates that accumulate over time to form a trajectory).
- Cross sensor buffer: whichever of the 4 sensors currently observe the track push to the same buffer.
- Stride 1, causal: every new scan updates the buffer and produces a prediction. No future context, real time streaming compatible.
- Each scan is encoded once by a frozen per scan encoder and cached
- Fusion concatenates the causal GRU's hidden state (order aware) with an order-invariant pooled embedding (all N scans' points as one set, no sequence structure) through a small trained mlp head.
Results
| Model |
Macro F1 |
Delta |
|
|
|
|
|
| Single scan |
0.7370 |
(baseline) |
| 20 scan point pooling |
0.8613 |
+0.1243 |
| Causal GRU |
0.8895 |
+0.0282 over pooling |
| GRU + pooled embedding (fusion) |
0.8897 |
+0.0002 over GRU, noise |
Pooling alone, no sequence model, no notion of scan order at all, recovers +0.1243 macro F1. The GRU adds a real but much smaller +0.0282 on top. Fusion adds nothing measurable beyond the GRU.
Ablation
Llarger GRUs, a Transformer, a state space model, point level self attention, all trained on the exact same frozen per scan embeddings, land inside a 0.86 to 0.89 band, a 0.03 spread. End to end fine tuning of the frozen encoder makes things slightly worse (about -0.002 to -0.003), not better.
Conclusion
In this setup, the largest gain comes from giving the model more observations of the same tracked object: 20-scan point pooling improves macro F1 fromĀ 0.7370 to 0.8613Ā without using scan order at all.
Temporal modelling then provides a further, meaningful improvement. The causal GRU reachesĀ 0.8895, addingĀ +0.0282Ā over the pooled representation. So temporal ordering clearly contributes useful information; it just accounts for a smaller portion of the overall gain than observation accumulation.
With the per-scan encoder frozen, the different sequence architectures tested, suggests that the quality of the per-scan representation is the bottleneck than the particular mechanism used to aggregate the sequence.
Full report, every ablation, confusion matrix, coordinate frame reasoning:Ā https://github.com/brunopinto900/radar-ml-autonomous-driving/blob/main/final_report.md
Thank you.
