# A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications

arXiv: https://arxiv.org/abs/2006.15172 · HTML: https://mlsanity.com/paper/2006.15172/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 19 of 19)

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- [Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap](https://mlsanity.com/paper/2201.11676/) (2022)
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- [Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression](https://mlsanity.com/paper/2308.09065/) (2023)
- [On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation](https://mlsanity.com/paper/2308.02248/) (2023)
- [Marginally calibrated response distributions for end-to-end learning in autonomous driving](https://mlsanity.com/paper/2110.01050/) (2021)
- [A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models](https://mlsanity.com/paper/2201.03263/) (2022)
- [Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception](https://mlsanity.com/paper/2206.06838/) (2022)
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- [Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry](https://mlsanity.com/paper/2208.01705/) (2022)
- [On the calibration of underrepresented classes in LiDAR-based semantic segmentation](https://mlsanity.com/paper/2210.06811/) (2022)
- [Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation](https://mlsanity.com/paper/2404.06124/) (2024)
- [Deep Modeling of Non-Gaussian Aleatoric Uncertainty](https://mlsanity.com/paper/2405.20513/) (2024)
- [Automated C-Arm Positioning via Conformal Landmark Localization](https://mlsanity.com/paper/2510.16160/) (2025)
- [When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems](https://mlsanity.com/paper/2606.29115/) (2026)
- [Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice](https://mlsanity.com/paper/2311.05245/) (2023)
- [SAE: Single Architecture Ensemble Neural Networks](https://mlsanity.com/paper/2402.06580/) (2024)

## Abstract (arXiv)

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks, different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in data and models. However, uncertainty quantification in DNNs is still a challenging task. These methods require a higher computational load, a higher memory footprint, and introduce extra latency, which can be prohibitive in safety-critical applications. In this paper, we provide a brief and comparative survey of methods for uncertainty quantification in DNNs along with existing metrics to evaluate uncertainty predictions. We are particularly interested in understanding the advantages and downsides of each method for specific AV tasks and types of uncertainty sources.