# Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems

arXiv: https://arxiv.org/abs/2110.03506 · HTML: https://mlsanity.com/paper/2110.03506/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 27 of 27)

- [Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination](https://mlsanity.com/paper/2405.00846/) (2024)
- [Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving](https://mlsanity.com/paper/2405.01691/) (2024)
- [Statistically Assuring Safety of Control Systems using Ensembles of Safety Filters and Conformal Prediction](https://mlsanity.com/paper/2511.07899/) (2025)
- [Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles](https://mlsanity.com/paper/2405.06770/) (2024)
- [EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems](https://mlsanity.com/paper/2604.11174/) (2026)
- [Safety Filter Design for Neural Network Systems via Convex Optimization](https://mlsanity.com/paper/2308.08086/) (2023)
- [End-to-End Imitation Learning for Optimal Asteroid Proximity Operations](https://mlsanity.com/paper/2502.01034/) (2025)
- [Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with a Proof-of-Concept Evaluation on Embodied Agents](https://mlsanity.com/paper/2604.08059/) (2026)
- [Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance](https://mlsanity.com/paper/2605.12561/) (2026)
- [Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution](https://mlsanity.com/paper/2608.16891/) (2026)
- [Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning](https://mlsanity.com/paper/2310.04288/) (2023)
- [Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control](https://mlsanity.com/paper/2311.08943/) (2023)
- [Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies](https://mlsanity.com/paper/2405.06771/) (2024)
- [Stacked Universal Successor Feature Approximators for Safety in Reinforcement Learning](https://mlsanity.com/paper/2409.04641/) (2024)
- [A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing](https://mlsanity.com/paper/2504.15226/) (2025)
- [Meta Policy Switching for Secure UAV Deconfliction in Adversarial Airspace](https://mlsanity.com/paper/2506.21127/) (2025)
- [Cognition Envelopes for Bounded Decision Making in Autonomous UAS Operations](https://mlsanity.com/paper/2510.26905/) (2025)
- [The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety](https://mlsanity.com/paper/2603.02259/) (2026)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [Can Predicted Dynamics Exist in the Physical World?](https://mlsanity.com/paper/2606.00089/) (2026)
- [Ceci n'est pas une pipe: AI systems as semantic abstractions](https://mlsanity.com/paper/2607.09489/) (2026)
- [RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control](https://mlsanity.com/paper/2607.17951/) (2026)
- [End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers](https://mlsanity.com/paper/2607.20674/) (2026)
- [Safety-Gated Agentic Supervisory Control on a Coupled Distillation Benchmark: Regime Map, Auditable Gate, and Co-Design Findings](https://mlsanity.com/paper/2607.27849/) (2026)
- [CUBICS: Situation-aware performance estimation for safety-relevant ML components](https://mlsanity.com/paper/2608.16564/) (2026)
- [Generator-Independent Runtime Assurance under Partial Observation](https://mlsanity.com/paper/2609.06036/) (2026)
- [AI Harness: Certification under Proposal-Conditioned Information for Foundation-Model Agents](https://mlsanity.com/paper/2609.32184/) (2026)

## Abstract (arXiv)

Run Time Assurance (RTA) Systems are online verification mechanisms that filter an unverified primary controller output to ensure system safety. The primary control may come from a human operator, an advanced control approach, or an autonomous control approach that cannot be verified to the same level as simpler control systems designs. The critical feature of RTA systems is their ability to alter unsafe control inputs explicitly to assure safety. In many cases, RTA systems can functionally be described as containing a monitor that watches the state of the system and output of a primary controller, and a backup controller that replaces or modifies control input when necessary to assure safety. An important quality of an RTA system is that the assurance mechanism is constructed in a way that is entirely agnostic to the underlying structure of the primary controller. By effectively decoupling the enforcement of safety constraints from performance-related objectives, RTA offers a number of useful advantages over traditional (offline) verification. This article provides a tutorial on developing RTA systems.