OmniSCS：通过完全可编辑的驾驶世界实现自动驾驶的全场景安全关键场景合成
OmniSCS: Omni Safety-Critical Scenario Synthesis for Autonomous Driving via a Fully Editable Driving World
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中文总结 AI 辅助
研究针对自动驾驶安全关键场景合成难题，提出OmniSCS系统，通过完全可编辑驾驶世界构建和SCS合成两个模块，保持数据保真度，在多数据集实验中表现出色，能增强算法并支持实时闭环测试，为SCS优化测试提供高效方案。
中文摘要 AI 辅助
安全关键场景（SCS）的合成及其通过闭环模拟的评估对于开发强大的自动驾驶系统至关重要。该过程的一个关键方面涉及在现有场景中编辑外观和轨迹层面的智能体状态。然而，当前方法在场景编辑后难以保持数据保真度，且无法通过此类修改高效生成高质量SCS。为克服这些限制，我们提出OmniSCS，这是一个创新系统，能生成具有高物理保真度的逼真SCS，并在合成环境中进行闭环测试。OmniSCS包括两个关键模块：1）完全可编辑驾驶世界构建模块，通过双策略智能体重建和深度细化背景重建方法在场景编辑期间保持高保真智能体外观和背景。2）SCS合成模块，便于对象插入和智能体轨迹编辑以合成多样SCS并保持数据保真度。在nuScenes、Waymo和KITTI数据集上的实验表明，OmniSCS在编辑场景保真度方面优于现有方法。我们进一步验证了其增强自动驾驶算法和支持实时（13Hz）闭环测试的能力。总体而言，OmniSCS为自动驾驶中的SCS优化和测试提供了更安全、有效且经济高效的解决方案。
英文摘要
The synthesis of safety-critical scenarios (SCS) and their evaluation through closed-loop simulations are crucial for developing robust autonomous driving systems. A key aspect of this process involves editing agent states in both appearance and trajectory levels within existing scenes. However, current methods struggle to preserve data fidelity after scene editing and fail to efficiently generate high-quality SCS through such modifications. To overcome these limitations, we propose OmniSCS, an innovative system that generates photorealistic SCS with high physical fidelity while enabling closed-loop testing in synthetic environments. OmniSCS comprises two key modules: 1) A Fully Editable Driving World Construction module that maintains high-fidelity agent appearance and background during scene editing via dual-strategy agent reconstruction and depth-refinement background reconstruction methods. 2) A SCS Synthesis module that facilitates object insertion and agent trajectory editing to synthesize diverse SCS while preserving data fidelity. Experiments on nuScenes, Waymo, and KITTI datasets show that OmniSCS outperforms state-of-the-art methods in edited scene fidelity. We further validate its ability to enhance autonomous driving algorithms and support real-time (13Hz) closed-loop testing. Overall, OmniSCS provides a safer, more effective, and cost-efficient solution for SCS optimization and testing in autonomous driving.
