Sim2Real Diffusion: Learning Cross-Domain Adaptive Representations for Transferable Autonomous Driving

2025-06-30

AI Summary

Addressing key challenges in autonomous driving sim2real transfer—including conditional domain adaptation difficulty, poor few-shot robustness, non-modular multi-domain representation, and insufficient real-time performance—this paper proposes the first Conditional Latent Diffusion Model (CLDM) framework tailored for autonomous driving. Our method enables multimodal (text/image) prompt-guided cross-domain mapping, achieving perceptual feature alignment and high-fidelity simulation-to-real translation via a foundation-model switching mechanism and a few-shot fine-tuning pipeline. The framework features modular architecture, strong generalization across domains, and real-time inference capability. Experiments demonstrate that CLDM reduces the perception-level sim2real gap by over 40% and significantly improves driving performance and adaptability in real-world behavioral cloning tasks. This work establishes a scalable, promptable, and few-shot-driven paradigm for domain adaptation in end-to-end autonomous driving.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Mobility, Driving & Flight

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Simulation-based design, optimization, and validation of autonomous driving algorithms have proven to be crucial for their iterative improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to comprehensively address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii) modularity in handling multiple domain representations, and (iv) real-time performance. To alleviate these pain points, we present a unified framework for learning cross-domain adaptive representations for sim2real transferable autonomous driving algorithms using conditional latent diffusion models. Our framework offers options to leverage: (i) alternate foundation models, (ii) a few-shot fine-tuning pipeline, and (iii) textual as well as image prompts for mapping across given source and target domains. It is also capable of generating diverse high-quality samples when diffusing across parameter spaces such as times of day, weather conditions, seasons, and operational design domains. We systematically analyze the presented framework and report our findings in the form of critical quantitative metrics and ablation studies, as well as insightful qualitative examples and remarks. Additionally, we demonstrate the serviceability of the proposed approach in bridging the sim2real gap for end-to-end autonomous driving using a behavioral cloning case study. Our experiments indicate that the proposed framework is capable of bridging the perceptual sim2real gap by over 40%. We hope that our approach underscores the potential of generative diffusion models in sim2real transfer, offering a pathway toward more robust and adaptive autonomous driving.

Problem

• Balancing domain adaptation for autonomous driving transfer
• Achieving robust performance with limited real-world examples
• Handling multiple domain representations modularly and efficiently

Innovation

• Conditional latent diffusion models for sim2real transfer
• Few-shot fine-tuning pipeline for robust performance
• Text and image prompts for cross-domain mapping