# Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving

arXiv: https://arxiv.org/abs/1903.03438 · HTML: https://mlsanity.com/paper/1903.03438/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks](https://mlsanity.com/paper/2103.15580/) (2021)
- [Safety of the Intended Driving Behavior Using Rulebooks](https://mlsanity.com/paper/2105.04472/) (2021)
- [PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios](https://mlsanity.com/paper/2211.03402/) (2022)
- [Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks](https://mlsanity.com/paper/2204.12378/) (2022)
- [SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving](https://mlsanity.com/paper/2211.04009/) (2022)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems](https://mlsanity.com/paper/2204.12402/) (2022)
- [Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey](https://mlsanity.com/paper/2111.14324/) (2021)
- [An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems](https://mlsanity.com/paper/2308.01369/) (2023)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)
- [Data Requirement Goal Modeling for Machine Learning Systems](https://mlsanity.com/paper/2504.07664/) (2025)

## Abstract (arXiv)

Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation. This position paper identifies (1) perceptual uncertainty as a performance measure used to define safety requirements and (2) its influence factors when using supervised ML. This work is a first step towards a framework for measuring and controling the effects of these factors and supplying evidence to support claims about perceptual uncertainty.