# Practical Solutions for Machine Learning Safety in Autonomous Vehicles

arXiv: https://arxiv.org/abs/1912.09630 · HTML: https://mlsanity.com/paper/1912.09630/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 15 of 15)

- [Taxonomy of Machine Learning Safety: A Survey and Primer](https://mlsanity.com/paper/2106.04823/) (2021)
- [A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks](https://mlsanity.com/paper/2112.00337/) (2021)
- [Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System](https://mlsanity.com/paper/2204.07874/) (2022)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [Unifying Evaluation of Machine Learning Safety Monitors](https://mlsanity.com/paper/2208.14660/) (2022)
- [Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition](https://mlsanity.com/paper/2103.00259/) (2021)
- [Shifting Transformation Learning for Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03899/) (2021)
- [Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications](https://mlsanity.com/paper/2402.08208/) (2024)
- [Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks](https://mlsanity.com/paper/2303.09731/) (2023)
- [MPC-Friendly Commitments for Publicly Verifiable Covert Security](https://mlsanity.com/paper/2109.07461/) (2021)
- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [Towards Audit Requirements for AI-based Systems in Mobility Applications](https://mlsanity.com/paper/2302.13567/) (2023)
- [Real-Time Incremental Explanations for Object Detectors in Autonomous Driving](https://mlsanity.com/paper/2408.11963/) (2024)
- [From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process](https://mlsanity.com/paper/2207.04969/) (2022)
- [Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection](https://mlsanity.com/paper/2406.15268/) (2024)

## Abstract (arXiv)

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.