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TRIM: Temporal Run-Time Monitoring of 3D Object Detection via Activation Statistics
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First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
TRIM: Temporal Run-Time Monitoring of 3D Object Detection via Activation Statistics
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.38613
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.38613v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
Reliable perception is essential to intelligent transportation systems, yet deep neural networks can silently miss safety-critical objects while continuing to produce apparently plausible outputs. Existing run-time monitors for 3D object detection generally assess each frame independently and therefore cannot exploit the temporal evolution of occlusion, point sparsity, and scene complexity. We present TRIM, a lightweight temporal monitor that detects missed-object failures without modifying or retraining the underlying detector. TRIM summarises intermediate activations using compact channel-wise statistics and processes short sequences of these summaries with a temporal convolutional network. Experiments on NuScenes show that temporal monitoring improves AUROC from 0.686 to 0.704 and reduces the false alarm rate at 95% recall from 0.818 to 0.759 compared with single-frame monitoring. Early and intermediate backbone features provide the strongest monitoring signal, while the temporal inference head requires only 0.159 ms on a CPU.