# Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

arXiv: https://arxiv.org/abs/2204.07874 · HTML: https://mlsanity.com/paper/2204.07874/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 92 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [TIDE: A General Toolbox for Identifying Object Detection Errors](https://mlsanity.com/paper/2008.08115/) (2020)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2112.11255/) (2021)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks](https://mlsanity.com/paper/2001.08001/) (2020)
- [The Role of Explainability in Assuring Safety of Machine Learning in Healthcare](https://mlsanity.com/paper/2109.00520/) (2021)
- [Monitoring and explainability of models in production](https://mlsanity.com/paper/2007.06299/) (2020)
- [Can Offline Testing of Deep Neural Networks Replace Their Online Testing?](https://mlsanity.com/paper/2101.11118/) (2021)
- [Towards Structured Evaluation of Deep Neural Network Supervisors](https://mlsanity.com/paper/1903.01263/) (2019)
- [Practical Solutions for Machine Learning Safety in Autonomous Vehicles](https://mlsanity.com/paper/1912.09630/) (2019)
- [Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators](https://mlsanity.com/paper/2012.06822/) (2020)
- [Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)](https://mlsanity.com/paper/2012.06511/) (2020)
- [Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL](https://mlsanity.com/paper/2109.07960/) (2021)
- [Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2103.09051/) (2021)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)
- [Practical relevance of software engineering research: Synthesizing the community's voice](https://mlsanity.com/paper/1812.01395/) (2018)
- [A Functional Safety Assessment Method for Cooperative Automotive Architecture](https://mlsanity.com/paper/2104.13729/) (2021)

## Built on by (most cited first, 14 of 14)

- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets](https://mlsanity.com/paper/2401.17013/) (2024)
- [System Safety Monitoring of Learned Components Using Temporal Metric Forecasting](https://mlsanity.com/paper/2405.13254/) (2024)
- [GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System](https://mlsanity.com/paper/2409.05346/) (2024)
- [Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels](https://mlsanity.com/paper/2504.19816/) (2025)
- [EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits](https://mlsanity.com/paper/2408.02052/) (2024)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves](https://mlsanity.com/paper/2408.00768/) (2024)
- [Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring](https://mlsanity.com/paper/2412.15948/) (2024)
- [RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification](https://mlsanity.com/paper/2604.17391/) (2026)
- [Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving](https://mlsanity.com/paper/2308.07106/) (2023)
- [A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems](https://mlsanity.com/paper/2503.02498/) (2025)
- [PCICF: A Pedestrian Crossing Identification and Classification Framework](https://mlsanity.com/paper/2509.24386/) (2025)
- [Safety Case Patterns for VLA-based driving systems: Insights from SimLingo](https://mlsanity.com/paper/2603.16013/) (2026)

## Abstract (arXiv)

Integration of Machine Learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system. This paper reports results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic emergency braking demonstrator running in an industry-grade simulator. We demonstrate an application of AMLAS on SMIRK for a minimalistic operational design domain, i.e., we share a complete safety case for its integrated ML-based component. Finally, we report lessons learned and provide both SMIRK and the safety case under an open-source licence for the research community to reuse.