Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
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Abstract (arXiv)
The increasing levels of software- and data-intensive driving automation call for an evolution of automotive software testing. As a recommended practice of the Verification and Validation (V&V) process of ISO/PAS 21448, a candidate standard for safety of the intended functionality for road vehicles, simulation-based testing has the potential to reduce both risks and costs. There is a growing body of research on devising test automation techniques using simulators for Advanced Driver-Assistance Systems (ADAS). However, how similar are the results if the same test scenarios are executed in different simulators? We conduct a replication study of applying a Search-Based Software Testing (SBST) solution to a real-world ADAS (PeVi, a pedestrian vision detection system) using two different commercial simulators, namely, TASS/Siemens PreScan and ESI Pro-SiVIC. Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators. We show that SBST can be used to effectively and efficiently generate critical test scenarios in both simulators, and the test results obtained from the two simulators can reveal several weaknesses of the ADAS under test. However, executing the same test scenarios in the two simulators leads to notable differences in the details of the test outputs, in particular, related to (1) safety violations revealed by tests, and (2) dynamics of cars and pedestrians. Based on our findings, we recommend future V&V plans to include multiple simulators to support robust simulation-based testing and to base test objectives on measures that are less dependant on the internals of the simulators.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 2; 2025: 2.
12 papers.
- 2021Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systemscited by 36
- 2022Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake Systemcited by 14
- 2021Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVLcited by 8
- 2023Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testingcited by 8
- 2023Boundary State Generation for Testing and Improvement of Autonomous Driving Systemscited by 6
- 2022Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testingcited by 4
- 2023Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systemscited by 4
- 2024Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systemscited by 4
- 2025Benchmarking Image Perturbations for Testing Automated Driving Assistance Systemscited by 4
- 2024Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?cited by 1
- 2025Simulator Ensembles for Trustworthy Autonomous Driving Systems Testingcited by 1
- 2022Safety Analysis of Autonomous Driving Systems Based on Model Learningcited by 0
- 2025Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing11 Mar 2025
- 2025Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems21 Jan 2025
- 2024Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?15 Oct 2024
- 2024Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems25 Aug 2024
- 2023Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems30 Nov 2023
- 2023Boundary State Generation for Testing and Improvement of Autonomous Driving Systems20 Jul 2023
- 2023Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing14 May 2023
- 2022Safety Analysis of Autonomous Driving Systems Based on Model Learning23 Nov 2022
- 2022Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System16 Apr 2022
- 2022Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing22 Mar 2022
- 2021Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems21 Dec 2021
- 2021Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL16 Sep 2021