# Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

arXiv: https://arxiv.org/abs/2202.13510 · HTML: https://mlsanity.com/paper/2202.13510/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Scenic: A Language for Scenario Specification and Scene Generation](https://mlsanity.com/paper/1809.09310/) (2018)
- [Striking the Right Balance with Uncertainty](https://mlsanity.com/paper/1901.07590/) (2019)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method](https://mlsanity.com/paper/2003.01197/) (2020)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE](https://mlsanity.com/paper/2003.08740/) (2020)
- [ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems](https://mlsanity.com/paper/2102.09419/) (2021)
- [Parallel and Multi-Objective Falsification with Scenic and VerifAI](https://mlsanity.com/paper/2107.04164/) (2021)

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

- [ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA](https://mlsanity.com/paper/2208.06309/) (2022)
- [PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving](https://mlsanity.com/paper/2511.07292/) (2025)
- [Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing](https://mlsanity.com/paper/2407.04359/) (2024)
- [Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?](https://mlsanity.com/paper/2410.11769/) (2024)

## Abstract (arXiv)

Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runtime safety components like Out-of-Distribution detectors and risk estimators, which require labeled data from different operating modes of the system that belong to scenes with adverse operating conditions, sensors, and actuator faults. Collecting real-world data of these scenes can be expensive and sometimes not feasible. So, scenario description languages with samplers like random and grid search are available to generate synthetic data from simulators, replicating these real-world scenes. However, we point out three limitations in using these conventional samplers. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that are often not included. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is necessary for better search space coverage. We present a scene generation approach with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO), which extend the conventional random search and Bayesian Optimization search to include the limitations. Also, to facilitate the samplers, we use a risk-based metric that evaluates how risky the scene was for the system. We demonstrate our approach using an Autonomous Vehicle example in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search. Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers, respectively.