# Experimental Resilience Assessment of An Open-Source Driving Agent

arXiv: https://arxiv.org/abs/1807.06172 · HTML: https://mlsanity.com/paper/1807.06172/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework](https://mlsanity.com/paper/2308.03538/) (2023)
- [A Transfer Learning Framework for Proactive Ramp Metering Performance Assessment](https://mlsanity.com/paper/2308.03542/) (2023)
- [Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems](https://mlsanity.com/paper/2307.08939/) (2023)
- [Spatiotemporal-Aware Bit-Flip Injection on DNN-based Advanced Driver Assistance Systems (extended version)](https://mlsanity.com/paper/2604.03753/) (2026)

## Abstract (arXiv)

Autonomous vehicles (AV) depend on the sensors like RADAR and camera for the perception of the environment, path planning, and control. With the increasing autonomy and interactions with the complex environment, there have been growing concerns regarding the safety and reliability of AVs. This paper presents a Systems-Theoretic Process Analysis (STPA) based fault injection framework to assess the resilience of an open-source driving agent, called openpilot, under different environmental conditions and faults affecting sensor data. To increase the coverage of unsafe scenarios during testing, we use a strategic software fault-injection approach where the triggers for injecting the faults are derived from the unsafe scenarios identified during the high-level hazard analysis of the system. The experimental results show that the proposed strategic fault injection approach increases the hazard coverage compared to random fault injection and, thus, can help with more effective simulation of safety-critical faults and testing of AVs. In addition, the paper provides insights on the performance of openpilot safety mechanisms and its ability in timely detection and recovery from faulty inputs.