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

arXiv: https://arxiv.org/abs/2306.02631 · HTML: https://mlsanity.com/paper/2306.02631/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2023.

## Built on by (most cited first, 5 of 5)

- [Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steering](https://mlsanity.com/paper/2511.01223/) (2025)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [On the Assessment of Sensitivity of Autonomous Vehicle Perception](https://mlsanity.com/paper/2602.00314/) (2026)
- [Improving Object Detection by Modifying Synthetic Data with Explainable AI](https://mlsanity.com/paper/2412.01477/) (2024)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)

## Abstract (arXiv)

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.