# Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving

arXiv: https://arxiv.org/abs/2107.04991 · HTML: https://mlsanity.com/paper/2107.04991/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://mlsanity.com/paper/1804.05132/) (2018)
- [Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control](https://mlsanity.com/paper/1811.06817/) (2018)
- [Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection](https://mlsanity.com/paper/1809.06006/) (2018)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Notes on the Behavior of MC Dropout](https://mlsanity.com/paper/2008.02627/) (2020)
- [MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection](https://mlsanity.com/paper/2010.01695/) (2020)
- [Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification](https://mlsanity.com/paper/2101.00982/) (2020)
- [Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning](https://mlsanity.com/paper/2012.06390/) (2020)

## Built on by (most cited first, 10 of 10)

- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots](https://mlsanity.com/paper/2507.17049/) (2025)
- [Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing](https://mlsanity.com/paper/2207.10495/) (2022)
- [An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions](https://mlsanity.com/paper/2110.03260/) (2021)
- [UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking](https://mlsanity.com/paper/2402.12303/) (2024)
- [Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines](https://mlsanity.com/paper/2212.07118/) (2022)
- [LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds](https://mlsanity.com/paper/2306.07835/) (2023)
- [Assessing the Uncertainty and Robustness of the Laptop Refurbishing Software](https://mlsanity.com/paper/2409.03782/) (2024)
- [Managing Uncertainty in LLM-based Multi-Agent System Operation](https://mlsanity.com/paper/2602.23005/) (2026)
- [Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks](https://mlsanity.com/paper/2304.02654/) (2023)

## Abstract (arXiv)

Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for decision making for object recognition, adjusting speed, etc. A mistake in such decision making can be damaging; thus, it is vital to measure the reliability of decisions made by such prediction models via uncertainty measurement. Uncertainty, in deep learning models, is often measured for classification problems. However, deep learning models in autonomous driving are often multi-output regression models. Hence, we propose a novel method called PURE (Prediction sURface uncErtainty) for measuring prediction uncertainty of such regression models. We formulate the object recognition problem as a regression model with more than one outputs for finding object locations in a 2-dimensional camera view. For evaluation, we modified three widely-applied object recognition models (i.e., YoLo, SSD300 and SSD512) and used the KITTI, Stanford Cars, Berkeley DeepDrive, and NEXET datasets. Results showed the statistically significant negative correlation between prediction surface uncertainty and prediction accuracy suggesting that uncertainty significantly impacts the decisions made by autonomous driving.