# UniSTPA: A Safety Analysis Framework for End-to-End Autonomous Driving

arXiv: https://arxiv.org/abs/2505.15005 · HTML: https://mlsanity.com/paper/2505.15005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 21 May 2025.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 4 of 4)

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [STPA for Learning-Enabled Systems: A Survey and A New Practice](https://mlsanity.com/paper/2302.10588/) (2023)
- [Using STPA in Compliance with ISO 26262 for Developing a Safe Architecture for Fully Automated Vehicles](https://mlsanity.com/paper/1703.03657/) (2017)

## Abstract (arXiv)

As autonomous driving technology continues to advance, end-to-end models have attracted considerable attention owing to their superior generalisation capability. Nevertheless, such learning-based systems entail numerous safety risks throughout development and on-road deployment, and existing safety-analysis methods struggle to identify these risks comprehensively. To address this gap, we propose the Unified System Theoretic Process Analysis (UniSTPA) framework, which extends the scope of STPA from the operational phase to the entire lifecycle of an end-to-end autonomous driving system, including information gathering, data preparation, closed loop training, verification, and deployment. UniSTPA performs hazard analysis not only at the component level but also within the model's internal layers, thereby enabling fine-grained assessment of inter and intra module interactions. Using a highway Navigate on Autopilot function as a case study, UniSTPA uncovers multi-stage hazards overlooked by conventional approaches including scene design defects, sensor fusion biases, and internal model flaws, through multi-level causal analysis, traces these hazards to deeper issues such as data quality, network architecture, and optimisation objectives. The analysis result are used to construct a safety monitoring and safety response mechanism that supports continuous improvement from hazard identification to system optimisation. The proposed framework thus offers both theoretical and practical guidance for the safe development and deployment of end-to-end autonomous driving systems.