Driving scene manipulation with sensor data is emerging as a promising alternative to traditional virtual driving simulators. However, existing frameworks struggle to generate realistic scenarios efficiently due to limited editing capabilities. To address these challenges, we present SIMSplat, a predictive driving scene editor with language-aligned Gaussian splatting. As a language-controlled editor, SIMSplat enables intuitive manipulation using natural language prompts. By aligning language with Gaussian-reconstructed scenes, it further supports direct querying of road objects, allowing precise and flexible editing. Our method provides detailed object-level editing, including adding new objects and modifying the trajectories of both vehicles and pedestrians, while also incorporating predictive path refinement through multi-agent motion prediction to generate realistic interactions among all agents in the scene. Experiments on the Waymo dataset demonstrate SIMSplat's extensive editing capabilities and adaptability across a wide range of scenarios. Project page: https://sungyeonparkk.github.io/simsplat/

翻译：利用传感器数据进行驾驶场景操控正成为传统虚拟驾驶模拟器的一种有前景的替代方案。然而，由于编辑能力有限，现有框架难以高效生成逼真场景。为应对这些挑战，我们提出了SIMSplat，一种基于语言对齐高斯溅射的预测性驾驶场景编辑器。作为一个语言控制的编辑器，SIMSplat支持使用自然语言提示进行直观操控。通过将语言与高斯重建场景对齐，它进一步支持对道路对象的直接查询，从而实现精确且灵活的编辑。我们的方法提供了详细的对象级编辑功能，包括添加新对象以及修改车辆和行人的轨迹，同时还通过多智能体运动预测融入预测性路径优化，以生成场景中所有智能体之间逼真的交互。在Waymo数据集上的实验证明了SIMSplat广泛的编辑能力和跨多种场景的适应性。项目页面：https://sungyeonparkk.github.io/simsplat/