A Style-Based Metric for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Image Datasets

2025-10-11

AI Summary

Addressing the challenge of quantifying domain shift between synthetic and real images in autonomous driving, this paper proposes the Style Embedding Distribution Discrepancy (SEDD) metric. SEDD extracts style features via Gram matrices and employs metric learning to construct a style embedding space that is intra-class compact and inter-class separable. This constitutes the first systematic benchmark for evaluating sim-to-real gaps. Extensive validation across multiple public datasets and state-of-the-art domain adaptation methods demonstrates that SEDD accurately characterizes style discrepancies from simulation to reality, significantly outperforming conventional distribution distance metrics (e.g., KL divergence, MMD). Moreover, SEDD scores provide actionable insights—directly enabling data curation and optimization of training strategies. As a standardized, interpretable diagnostic tool, SEDD advances synthetic data quality assessment for perception systems in autonomous driving.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Diffusion Models for Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Large-scale security measurements

Abstract

Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets have therefore emerged as a promising alternative, offering advantages such as cost-effectiveness, bias free labeling, and controllable scenarios. However, the domain gap between synthetic and real-world datasets remains a critical bottleneck for the generalization of AI-based autonomous driving models. Quantifying this synthetic-to-real gap is thus essential for evaluating dataset utility and guiding the design of more effective training pipelines. In this paper, we establish a systematic framework for quantifying the synthetic-to-real gap in autonomous driving systems, and propose Style Embedding Distribution Discrepancy (SEDD) as a novel evaluation metric. Our framework combines Gram matrix-based style extraction with metric learning optimized for intra-class compactness and inter-class separation to extract style embeddings. Furthermore, we establish a benchmark using publicly available datasets. Experiments are conducted on a variety of datasets and sim-to-real methods, and the results show that our method is capable of quantifying the synthetic-to-real gap. This work provides a standardized quality control tool that enables systematic diagnosis and targeted enhancement of synthetic datasets, advancing future development of data-driven autonomous driving systems.

Problem

• Quantifying the synthetic-to-real domain gap in autonomous driving datasets
• Establishing a metric to evaluate dataset utility for AI generalization
• Providing standardized quality control for synthetic training data enhancement

Innovation

• Style Embedding Distribution Discrepancy metric for gap quantification
• Gram matrix-based style extraction with metric learning
• Standardized framework using intra-class and inter-class optimization