In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully reflect the performance of entire systems, highlighting the need for more holistic assessment methods. This motivates the development of HUGSIM, a closed-loop, photo-realistic, and real-time simulator for evaluating autonomous driving algorithms. We achieve this by lifting captured 2D RGB images into the 3D space via 3D Gaussian Splatting, improving the rendering quality for closed-loop scenarios, and building the closed-loop environment. In terms of rendering, We tackle challenges of novel view synthesis in closed-loop scenarios, including viewpoint extrapolation and 360-degree vehicle rendering. Beyond novel view synthesis, HUGSIM further enables the full closed simulation loop, dynamically updating the ego and actor states and observations based on control commands. Moreover, HUGSIM offers a comprehensive benchmark across more than 70 sequences from KITTI-360, Waymo, nuScenes, and PandaSet, along with over 400 varying scenarios, providing a fair and realistic evaluation platform for existing autonomous driving algorithms. HUGSIM not only serves as an intuitive evaluation benchmark but also unlocks the potential for fine-tuning autonomous driving algorithms in a photorealistic closed-loop setting.

翻译：在过去的几十年里，自动驾驶算法在感知、规划与控制方面取得了显著进展。然而，对单个组件的评估并不能完全反映整个系统的性能，这凸显了对更全面评估方法的需求。这推动了HUGSIM的开发，这是一个用于评估自动驾驶算法的闭环、逼真且实时的仿真器。我们通过基于3D高斯泼溅将捕获的2D RGB图像提升至3D空间，改善了闭环场景的渲染质量，并构建了闭环环境。在渲染方面，我们解决了闭环场景中新视角合成面临的挑战，包括视点外推和360度车辆渲染。除了新视角合成，HUGSIM进一步实现了完整的闭环仿真，能够根据控制指令动态更新自车与交通参与者的状态及观测。此外，HUGSIM提供了一个涵盖KITTI-360、Waymo、nuScenes和PandaSet数据集中超过70个序列以及400多种变化场景的综合基准，为现有自动驾驶算法提供了一个公平且真实的评估平台。HUGSIM不仅作为一个直观的评估基准，还解锁了在逼真闭环设置中微调自动驾驶算法的潜力。