  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.
