Neural Radiance Fields and Gaussian Splatting have emerged as powerful representations for reconstructing complex dynamic urban scenes from multi-view driving logs. By producing photorealistic and geometrically consistent renderings, these methods offer a foundation for closed-loop simulation as well as scalable data augmentation engine, enabling the synthesis of diverse viewpoints and conditions without the need for costly additional data collection in the real world. However, existing approaches are almost exclusively tailored to well-lit, daytime environments and struggle with the challenges of nighttime settings, where noise, strong flares, and moving light sources dominate the visual signal. To address these limitations, we propose NightSplat, a Gaussian Splatting framework specifically designed for high-fidelity reconstruction and novel view synthesis of nighttime driving scenes. Through dedicated components, our method explicitly models sensor noise, lens flare effects, and introduces a lightweight module to represent dynamic vehicle lights. Extensive evaluations on nuScenes and Waymo Open Dataset demonstrate that NightSplat significantly outperforms prior state-of-the-art baselines, both quantitatively and qualitatively, thereby extending simulation and testing to nighttime scenes.
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