Modern autonomous systems require extensive testing to ensure reliability and build trust in ground vehicles. However, testing these systems in the real-world is challenging due to the lack of large and diverse datasets, especially in edge cases. Therefore, simulations are necessary for their development and evaluation. However, existing open-source simulators often exhibit a significant gap between synthetic and real-world domains, leading to deteriorated mobility performance and reduced platform reliability when using simulation data. To address this issue, our Scoping Autonomous Vehicle Simulation (SAVeS) platform benchmarks the performance of simulated environments for autonomous ground vehicle testing between synthetic and real-world domains. Our platform aims to quantify the domain gap and enable researchers to develop and test autonomous systems in a controlled environment. Additionally, we propose using domain adaptation technologies to address the domain gap between synthetic and real-world data with our SAVeS$^+$ extension. Our results demonstrate that SAVeS$^+$ is effective in helping to close the gap between synthetic and real-world domains and yields comparable performance for models trained with processed synthetic datasets to those trained on real-world datasets of same scale. This paper highlights our efforts to quantify and address the domain gap between synthetic and real-world data for autonomy simulation. By enabling researchers to develop and test autonomous systems in a controlled environment, we hope to bring autonomy simulation one step closer to realization.

翻译：现代自主系统需要广泛测试以确保可靠性并建立对地面车辆的信任。然而，在真实世界中测试这些系统具有挑战性，因为缺乏大规模且多样化的数据集，尤其是在边缘场景中。因此，模拟对其开发和评估是必要的。然而，现有的开源模拟器通常在合成域和真实世界域之间表现出显著差距，导致使用模拟数据时移动性能下降和平台可靠性降低。为解决这一问题，我们的自动驾驶车辆模拟范围（SAVeS）平台对用于自主地面车辆测试的模拟环境在合成域和真实世界域之间的性能进行了基准测试。该平台旨在量化领域差距，并使研究人员能够在受控环境中开发和测试自主系统。此外，我们提出使用领域自适应技术来解决合成数据与真实世界数据之间的领域差距，并引入SAVeS$^+$扩展。结果表明，SAVeS$^+$有助于有效弥合合成域与真实世界域之间的差距，使经过处理的合成数据集训练的模型能够获得与同规模真实世界数据集训练的模型相当的性能。本文重点介绍了我们在量化并解决自主模拟中合成数据与真实世界数据之间领域差距方面所做的努力。通过使研究人员能够在受控环境中开发和测试自主系统，我们希望将自主模拟向实现更推进一步。