# ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems

arXiv: https://arxiv.org/abs/2102.09419 · HTML: https://mlsanity.com/paper/2102.09419/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA](https://mlsanity.com/paper/2208.06309/) (2022)
- [Confidence Composition for Monitors of Verification Assumptions](https://mlsanity.com/paper/2111.03782/) (2021)
- [Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems](https://mlsanity.com/paper/2202.13510/) (2022)
- [Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems](https://mlsanity.com/paper/2302.09750/) (2023)
- [Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2103.14172/) (2021)
- [Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems](https://mlsanity.com/paper/2307.13419/) (2023)
- [Towards provable probabilistic safety for scalable embodied AI systems](https://mlsanity.com/paper/2506.05171/) (2025)
- [Towards Continuous Assurance with Formal Verification and Assurance Cases](https://mlsanity.com/paper/2511.14805/) (2025)

## Abstract (arXiv)

Autonomous CPSs are often required to handle uncertainties and self-manage the system operation in response to problems and increasing risk in the operating paradigm. This risk may arise due to distribution shifts, environmental context, or failure of software or hardware components. Traditional techniques for risk assessment focus on design-time techniques such as hazard analysis, risk reduction, and assurance cases among others. However, these static, design-time techniques do not consider the dynamic contexts and failures the systems face at runtime. We hypothesize that this requires a dynamic assurance approach that computes the likelihood of unsafe conditions or system failures considering the safety requirements, assumptions made at design time, past failures in a given operating context, and the likelihood of system component failures. We introduce the ReSonAte dynamic risk estimation framework for autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs) which capture information about hazard propagation paths and control strategies. Our innovation is the extension of the BTD formalism with attributes for modeling the conditional relationships with the state of the system and environment. We also describe a technique for estimating these conditional relationships and equations for estimating risk based on the state of the system and environment. To help with this process, we provide a scenario modeling procedure that can use the prior distributions of the scenes and threat conditions to generate the data required for estimating the conditional relationships. To improve scalability and reduce the amount of data required, this process considers each control strategy in isolation and composes several single-variate distributions into one complete multi-variate distribution for the control strategy in question.