# Safety Shielding under Delayed Observation

arXiv: https://arxiv.org/abs/2307.02164 · HTML: https://mlsanity.com/paper/2307.02164/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safe Multi-Agent Reinforcement Learning via Shielding](https://mlsanity.com/paper/2101.11196/) (2021)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [Shield Synthesis: Runtime Enforcement for Reactive Systems](https://mlsanity.com/paper/1501.02573/) (2015)
- [Adaptive Shielding under Uncertainty](https://mlsanity.com/paper/2010.03842/) (2020)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [Online Shielding for Stochastic Systems](https://mlsanity.com/paper/2012.09539/) (2020)
- [Automata Learning meets Shielding](https://mlsanity.com/paper/2212.01838/) (2022)
- [Switching Controller Synthesis for Delay Hybrid Systems under Perturbations](https://mlsanity.com/paper/2103.11565/) (2021)

## Built on by (most cited first, 3 of 3)

- [QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI](https://mlsanity.com/paper/2507.15330/) (2025)
- [Efficient Dynamic Shielding for Parametric Safety Specifications](https://mlsanity.com/paper/2505.22104/) (2025)
- [Shielding for Higher-Order Safety](https://mlsanity.com/paper/2608.03662/) (2026)

## Abstract (arXiv)

Agents operating in physical environments need to be able to handle delays in the input and output signals since neither data transmission nor sensing or actuating the environment are instantaneous. Shields are correct-by-construction runtime enforcers that guarantee safe execution by correcting any action that may cause a violation of a formal safety specification. Besides providing safety guarantees, shields should interfere minimally with the agent. Therefore, shields should pick the safe corrective actions in such a way that future interferences are most likely minimized. Current shielding approaches do not consider possible delays in the input signals in their safety analyses. In this paper, we address this issue. We propose synthesis algorithms to compute delay-resilient shields that guarantee safety under worst-case assumptions on the delays of the input signals. We also introduce novel heuristics for deciding between multiple corrective actions, designed to minimize future shield interferences caused by delays. As a further contribution, we present the first integration of shields in a realistic driving simulator. We implemented our delayed shields in the driving simulator textscCarla. We shield potentially unsafe autonomous driving agents in different safety-critical scenarios and show the effect of delays on the safety analysis.