# Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving

arXiv: https://arxiv.org/abs/1909.12358 · HTML: https://mlsanity.com/paper/1909.12358/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 30 of 30)

- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors](https://mlsanity.com/paper/2101.05036/) (2021)
- [Uncertainty for Identifying Open-Set Errors in Visual Object Detection](https://mlsanity.com/paper/2104.01328/) (2021)
- [A Benchmark Study on Calibration](https://mlsanity.com/paper/2308.11838/) (2023)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection](https://mlsanity.com/paper/2207.01242/) (2022)
- [From Black-box to White-box: Examining Confidence Calibration under different Conditions](https://mlsanity.com/paper/2101.02971/) (2021)
- [Feature Clipping for Uncertainty Calibration](https://mlsanity.com/paper/2410.19796/) (2024)
- [Bayesian Confidence Calibration for Epistemic Uncertainty Modelling](https://mlsanity.com/paper/2109.10092/) (2021)
- [LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2206.00214/) (2022)
- [Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration](https://mlsanity.com/paper/2306.08981/) (2023)
- [Towards Calibrated Deep Clustering Network](https://mlsanity.com/paper/2403.02998/) (2024)
- [f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception](https://mlsanity.com/paper/2109.13913/) (2021)
- [Calibrating Perception Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2211.04340/) (2022)
- [Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns](https://mlsanity.com/paper/2404.07685/) (2024)
- [Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors](https://mlsanity.com/paper/2410.12295/) (2024)
- [Sample Margin-Aware Recalibration of Temperature Scaling](https://mlsanity.com/paper/2506.23492/) (2025)
- [Low-Rank Adaptation Redux for Large Models](https://mlsanity.com/paper/2604.21905/) (2026)
- [Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters](https://mlsanity.com/paper/2604.03388/) (2026)
- [Perception and Semantic Aware Regularization for Sequential Confidence Calibration](https://mlsanity.com/paper/2305.19498/) (2023)
- [A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms](https://mlsanity.com/paper/2307.01013/) (2023)
- [How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy](https://mlsanity.com/paper/2508.09346/) (2025)
- [Self-Aware Object Detection via Degradation Manifolds](https://mlsanity.com/paper/2602.18394/) (2026)
- [Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift](https://mlsanity.com/paper/2605.05328/) (2026)
- [Probabilistic 3d regression with projected huber distribution](https://mlsanity.com/paper/2303.05245/) (2023)
- [Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering](https://mlsanity.com/paper/2405.07600/) (2024)
- [Calibrating the Full Predictive Class Distribution of 3D Object Detectors for Autonomous Driving](https://mlsanity.com/paper/2510.01829/) (2025)
- [Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment](https://mlsanity.com/paper/2605.16087/) (2026)
- [Calibrating Probabilistic Object Detectors with Annotator Disagreement](https://mlsanity.com/paper/2605.24722/) (2026)
- [Instance-Level Post Hoc Uncertainty Quantification in Object Detection](https://mlsanity.com/paper/2606.04656/) (2026)

## Abstract (arXiv)

Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving. Recently, many methods have been proposed to model uncertainties in deep learning based object detectors. However, the estimated probabilities are often uncalibrated, which may lead to severe problems in safety critical scenarios. In this work, we identify such uncertainty miscalibration problems in a probabilistic LiDAR 3D object detection network, and propose three practical methods to significantly reduce errors in uncertainty calibration. Extensive experiments on several datasets show that our methods produce well-calibrated uncertainties, and generalize well between different datasets.