# Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing arXiv: https://arxiv.org/abs/2305.08060 ยท HTML: https://mlsanity.com/paper/2305.08060/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 92 references that are in the map. - First posted on arXiv 14 May 2023. ## Builds on (most cited first, 26 of 26) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016) - [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015) - [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019) - [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019) - [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017) - [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018) - [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015) - [PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving](https://mlsanity.com/paper/1907.04449/) (2019) - [A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale](https://mlsanity.com/paper/2012.05841/) (2020) - [Misbehaviour Prediction for Autonomous Driving Systems](https://mlsanity.com/paper/1910.04443/) (2019) - [Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing](https://mlsanity.com/paper/2007.02787/) (2020) - [Learning to Drive from Simulation without Real World Labels](https://mlsanity.com/paper/1812.03823/) (2018) - [Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2112.11255/) (2021) - [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022) - [DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search](https://mlsanity.com/paper/2107.06997/) (2021) - [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021) - [Can Offline Testing of Deep Neural Networks Replace Their Online Testing?](https://mlsanity.com/paper/2101.11118/) (2021) - [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles](https://mlsanity.com/paper/2109.06126/) (2021) - [DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing](https://mlsanity.com/paper/2211.01829/) (2022) - [Automated Unit Test Generation for Python](https://mlsanity.com/paper/2007.14049/) (2020) - [ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection](https://mlsanity.com/paper/1907.01051/) (2019) - [Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators](https://mlsanity.com/paper/2012.06822/) (2020) - [Robotics Software Engineering: A Perspective from the Service Robotics Domain](https://mlsanity.com/paper/2006.10608/) (2020) - [Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning](https://mlsanity.com/paper/2008.00715/) (2020) - [Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing](https://mlsanity.com/paper/2203.12026/) (2022) ## Built on by (most cited first, 9 of 9) - [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024) - [Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models](https://mlsanity.com/paper/2409.13661/) (2024) - [Boundary State Generation for Testing and Improvement of Autonomous Driving Systems](https://mlsanity.com/paper/2307.10590/) (2023) - [Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems](https://mlsanity.com/paper/2408.13950/) (2024) - [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025) - [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025) - [A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems](https://mlsanity.com/paper/2509.22379/) (2025) - [Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions](https://mlsanity.com/paper/2512.11887/) (2025) - [DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2610.04494/) (2026) ## Abstract (arXiv) Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we enhance simulation-based testing by introducing the notion of digital siblings, a multi-simulator approach that tests a given autonomous vehicle on multiple general-purpose simulators built with different technologies, that operate collectively as an ensemble in the testing process. We exemplify our approach on a case study focused on testing the lane-keeping component of an autonomous vehicle. We use two open-source simulators as digital siblings, and we empirically compare such a multi-simulator approach against a digital twin of a physical scaled autonomous vehicle on a large set of test cases. Our approach requires generating and running test cases for each individual simulator, in the form of sequences of road points. Then, test cases are migrated between simulators, using feature maps to characterize the exercised driving conditions. Finally, the joint predicted failure probability is computed, and a failure is reported only in cases of agreement among the siblings. Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin. We discuss the findings of our case study and detail how our approach can help researchers interested in automated testing of autonomous driving software.