DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing

End-to-end (E2E) autonomous driving algorithms requirerigorous closed-loop validation in simulation environments offering highvisual fidelity, strong interactivity, and real-time performance. Exist-ing approaches, from game engines to static neural rendering, inher-ently trade off these requirements and struggle with the dynamic scenecomposition essential for E2E testing. To bridge this gap, we proposea novel decoupled 3D Gaussian Splatting (3DGS) framework tailoredfor large-scale E2E evaluation. We fundamentally decompose scenes intoa high-fidelity static background and manipulable dynamic agents us-ing an object-centric canonical representation. To resolve resulting rep-resentational conflicts, we introduce three targeted modules: (1) assetcompression via perceptual pruning and vector quantization for real-time traffic rendering; (2) map-guided geometric registration leveragingsemantic topology to strictly align trajectories; and (3) proxy-based re-lighting transferring ambient illumination for seamless photometric inte-gration. Extensive experiments demonstrate that DecoupleGS achievesa balanced fidelity-efficiency trade-off, improves metric and photomet-ric consistency, and provides a practical closed-loop sensor simulationplatform for E2E autonomous driving evaluation.