# AVFI: Fault Injection for Autonomous Vehicles

arXiv: https://arxiv.org/abs/1907.01038 · HTML: https://mlsanity.com/paper/1907.01038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles](https://mlsanity.com/paper/2103.07403/) (2021)
- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Attacks and Faults Injection in Self-Driving Agents on the Carla Simulator -- Experience Report](https://mlsanity.com/paper/2202.12991/) (2022)
- [Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework](https://mlsanity.com/paper/2309.11274/) (2023)

## Abstract (arXiv)

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.