Bridging the Domain Gap between Synthetic and Real-World Data for Autonomous Driving

TL;DR

This paper introduces the SAVeS platform and SAVeS$^+$ extension to quantify and reduce the domain gap between synthetic and real-world data, improving autonomous vehicle testing in simulation environments.

Contribution

The paper presents a new benchmarking platform and domain adaptation techniques to bridge the synthetic-real domain gap for autonomous driving simulations.

Findings

SAVeS$^+$ effectively reduces the domain gap.

Models trained on processed synthetic data perform comparably to real-world data models.

The platform enables controlled testing and benchmarking for autonomous systems.

Abstract

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.