Instance-Level Post Hoc Uncertainty Quantification in Object Detection

Researchers propose MC-GLM, a novel method for quantifying uncertainty in object detection predictions without model retraining, using Laplace approximation and Monte Carlo sampling. The technique enables efficient, instance-level uncertainty estimates critical for autonomous driving safety, validated on the nuScenes dataset with CenterPoint detector.

Object detection uncertainty quantification addresses a fundamental challenge in deploying autonomous systems safely. Traditional approaches either require expensive model retraining or computational overhead through multiple backpropagations, creating friction between theoretical rigor and practical deployment. This research bridges that gap by introducing MC-GLM, which provides post hoc uncertainty estimates—meaning the method can be applied to already-trained models without modification.

The breakthrough lies in computational efficiency. Previous linearized inference methods demand multiple backward passes proportional to output instances, making them prohibitively slow for real-time autonomous driving. MC-GLM instead uses a constant number of Monte Carlo samples independent of instance count, enabling parallelization and practical scalability. This matters because autonomous vehicles process multiple object detections per frame across thousands of frames—efficiency directly impacts deployment feasibility.

The validation on nuScenes, a standard autonomous driving benchmark, demonstrates practical relevance. Quality uncertainty estimates enable safety-critical systems to flag low-confidence predictions and trigger appropriate fallback mechanisms. This capability strengthens the assurance case for autonomous vehicles, particularly for regulators and insurers evaluating deployment readiness.

The research addresses an underexplored intersection: most uncertainty quantification work focuses on classification, while object detection—requiring both localization and class prediction—poses distinct challenges. Instance-level quantification is essential because different objects in a scene may have varying detection confidence. As autonomous systems transition from development to production, post hoc methods that avoid retraining entire pipelines become increasingly valuable for incorporating safety improvements iteratively.

• →MC-GLM enables post hoc uncertainty quantification for object detection without retraining deployed models.
• →Constant-time Monte Carlo sampling independent of instance count enables real-time autonomous driving applications.
• →Instance-level uncertainty estimates allow safety systems to identify and handle low-confidence predictions appropriately.
• →Validation on nuScenes benchmark confirms practical effectiveness for autonomous vehicle perception pipelines.
• →The method addresses a critical gap between theoretical safety assurance and production deployment constraints.