# ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

arXiv: https://arxiv.org/abs/1907.01051 · HTML: https://mlsanity.com/paper/1907.01051/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 16 of 16)

- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Active causal structure learning with advice](https://mlsanity.com/paper/2305.19588/) (2023)
- [Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing](https://mlsanity.com/paper/2305.08060/) (2023)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [ISimDL: Importance Sampling-Driven Acceleration of Fault Injection Simulations for Evaluating the Robustness of Deep Learning](https://mlsanity.com/paper/2303.08035/) (2023)
- [Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems](https://mlsanity.com/paper/2307.08939/) (2023)
- [STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation](https://mlsanity.com/paper/2312.09559/) (2023)
- [High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models](https://mlsanity.com/paper/2407.21069/) (2024)
- [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025)
- [FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems](https://mlsanity.com/paper/2203.07276/) (2022)
- [Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles](https://mlsanity.com/paper/2306.07142/) (2023)
- [Towards Formal Fault Injection for Safety Assessment of Automated Systems](https://mlsanity.com/paper/2311.09810/) (2023)
- [Foundation Models for Rapid Autonomy Validation](https://mlsanity.com/paper/2411.03328/) (2024)
- [Spatiotemporal-Aware Bit-Flip Injection on DNN-based Advanced Driver Assistance Systems (extended version)](https://mlsanity.com/paper/2604.03753/) (2026)
- [Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems](https://mlsanity.com/paper/2605.08190/) (2026)

## Abstract (arXiv)

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not find any safety-critical faults