Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories. We present a deployment-friendly mitigation strategy that augments a 3D object detector with aleatoric uncertainty estimates and applies a two-sample z-test over short observation windows to separate true motion from jitter. Integrated into Autoware with minimal changes, the approach reuses existing data association for minimal compute overhead. Empirical results show parity with velocity thresholding on n...
| Authors | Cornelius Schröder, Žygimantas Marcinkus, Markus Lienkamp |
|---|---|
| Primary category | cs.RO |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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