RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning

Abstract

As end-to-end autonomous driving advances toward real-world deployment, ensuring the safety of autonomous vehicles (AVs) has become a critical requirement for their commercial viability. While rule-based AVs have traditionally undergone rigorous testing in both real-world and simulated environments before deployment, data-driven autonomous models are typically trained on real-world datasets, limiting their generalization to simulation environments. This poses a significant challenge for the development and testing of end-to-end autonomous driving. To address this issue, we propose Retrieval-Augmented Learning for Autonomous Driving (RALAD), a novel framework designed to bridge the real-to-sim gap in a cost-effective manner. RALAD consists of three key components: (1) domain adaptation via an enhanced Optimal Transport (OT) method, which retrieves the most similar scenarios between real and simulated environments; (2) feature fusion across similar scenarios, enabling the construction of a feature mapping between real-world and simulated domains; and (3) feature extraction freezing with fine-tuning on the fused features, allowing the model to learn simulation-specific characteristics through feature mapping. We evaluate RALAD on three monocular 3D object detection models, and the results demonstrate that our approach significantly improves model accuracy in simulation. Additionally, we use real autonomous vehicle for testing in real-world scenarios, and have established simulated scenes similar to reality for further testing, which illustrate the effectiveness of our method.