$\alpha$-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction

Sanbao Su, Nuo Chen, Felix Juefei-Xu, Chen Feng, Fei Miao

Keywords: Uncertainty Propagation, Semantic Occupancy Prediction, Conformal Prediction

TL;DR: To address the challenging camera-based 3D Semantic Occupancy Prediction (OCC) problem for autonomous driving, we recognize the problem from a fresh uncertainty quantification (UQ) perspective.

Abstract: In the realm of autonomous vehicle (AV) perception, comprehending 3D scenes is paramount for tasks such as planning and mapping. Camera-based 3D Semantic Occupancy Prediction (OCC) aims to infer scene geometry and semantics from limited observations. While it has gained popularity due to affordability and rich visual cues, existing methods often neglect the inherent uncertainty in models. To address this, we propose an uncertainty-aware camera-based 3D semantic occupancy prediction method ($\alpha$-OCC). Our approach includes an uncertainty propagation framework (Depth-UP) from depth models to enhance geometry completion (up to 11.58\% improvement) and semantic segmentation (up to 12.95\% improvement) for a variety of OCC models. Additionally, we propose a hierarchical conformal prediction (HCP) method to quantify OCC uncertainty, effectively addressing the high-level class imbalance in OCC datasets. On the geometry level, we present a novel KL-based score function that significantly improves the occupied recall of safety-critical classes (45\% improvement) with minimal performance overhead (3.4\% reduction). For uncertainty quantification, we demonstrate the ability to achieve smaller prediction set sizes while maintaining a defined coverage guarantee. Compared with baselines, it reduces up to 92\% set size. Our contributions represent significant advancements in OCC accuracy and robustness, marking a noteworthy step forward in autonomous perception systems.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 5249