# Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

arXiv: https://arxiv.org/abs/2302.09750 · HTML: https://mlsanity.com/paper/2302.09750/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [An approach to reachability analysis for feed-forward ReLU neural networks](https://mlsanity.com/paper/1706.07351/) (2017)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products](https://mlsanity.com/paper/1610.01256/) (2016)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Neural Simplex Architecture](https://mlsanity.com/paper/1908.00528/) (2019)
- [SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems](https://mlsanity.com/paper/1808.07921/) (2018)
- [RGB cameras failures and their effects in autonomous driving applications](https://mlsanity.com/paper/2008.05938/) (2020)
- [Hierarchical Planning for Resource Allocation in Emergency Response Systems](https://mlsanity.com/paper/2012.13300/) (2020)
- [ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems](https://mlsanity.com/paper/2102.09419/) (2021)
- [Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner](https://mlsanity.com/paper/2201.09140/) (2022)
- [An Online Decision-Theoretic Pipeline for Responder Dispatch](https://mlsanity.com/paper/1902.08274/) (2019)
- [The Black-Box Simplex Architecture for Runtime Assurance of Autonomous CPS](https://mlsanity.com/paper/2102.12981/) (2021)
- [A Component-Based Simplex Architecture for High-Assurance Cyber-Physical Systems](https://mlsanity.com/paper/1704.04759/) (2017)
- [Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems](https://mlsanity.com/paper/1902.02432/) (2019)
- [Blending Controllers via Multi-Objective Bandits](https://mlsanity.com/paper/2007.15755/) (2020)
- [Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search](https://mlsanity.com/paper/2202.13003/) (2022)

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

- [Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes](https://mlsanity.com/paper/2401.01841/) (2024)
- [Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction](https://mlsanity.com/paper/2502.21186/) (2025)
- [Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL](https://mlsanity.com/paper/2601.19499/) (2026)
- [Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control](https://mlsanity.com/paper/2605.02697/) (2026)

## Abstract (arXiv)

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system's safety, which limits the system's adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.