ObjectTransforms for Uncertainty Quantification and Reduction in Vision-Based Perception for Autonomous Vehicles

Abstract

Reliable perception is fundamental for safety critical decision making in autonomous driving. Yet, vision based object detector neural networks remain vulnerable to uncertainty arising from issues such as data bias and distributional shifts. In this paper, we introduce ObjectTransforms, a technique for quantifying and reducing uncertainty in vision based object detection through object specific transformations at both training and inference times. At training time, ObjectTransforms perform color space perturbations on individual objects, improving robustness to lighting and color variations. ObjectTransforms also uses diffusion models to generate realistic, diverse pedestrian instances. At inference time, object perturbations are applied to detected objects and the variance of detection scores are used to quantify predictive uncertainty in real time. This uncertainty signal is then used to filter out false positives and also recover false negatives, improving the overall precision recall curve. Experiments with YOLOv8 on the NuImages 10K dataset demonstrate that our method yields notable accuracy improvements and uncertainty reduction across all object classes during training, while predicting desirably higher uncertainty values for false positives as compared to true positives during inference. Our results highlight the potential of ObjectTransforms as a lightweight yet effective mechanism for reducing and quantifying uncertainty in vision-based perception during training and inference respectively.