Open Access

Lennart BramlageORCiD, Cristóbal CurioORCiD

• Uncertainty quantification (UQ) is critical for deploying dense perception in intelligent vehicles, yet it is rarely included in off-the-shelf regression networks because joint UQ training often couples the likelihood to the predictor and degrades point-estimate accuracy. We frame IO-CUE as a post-hoc behavioral probe for frozen, black-box predictors: an auxiliary model conditioned on both inputs and outputs, trained with a detached maximum-likelihood objective on mild, structured perturbations. These probes expose how the base model’s predictions deform off-manifold, allowing IO-CUE to convert output-space deviations into calibrated uncertainty and to capture quasi-epistemic signatures without modifying the base predictor. Across synthetic tasks and dense depth benchmarks (NYU and driving datasets), IO-CUE matches or improves calibration and OOD sensitivity relative to ensembles, Laplace approximations, concurrent MLE variants, and output-only post-hoc baselines. Crucially, it generalizes to unseen perturbations and transfers across base models, revealing stable, dataset-agnostic output-space regularities characteristic of the model family. These behavioral signatures enable practical, low-overhead OOD detection in intelligent-vehicle settings without retraining the base network. Code: https://github.com/biggzlar/IO-CUE.