The synthesis of safety-critical scenarios (SCS) and their evaluation through closed-loop simulations are crucial for the development of robust autonomous driving systems. A key aspect of this process involves editing agent states at 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:
- 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.
- 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.
Blogger's Review: The introduction of the OmniSCS system offers an innovative approach in the autonomous driving domain, enhancing safety and efficiency through high-fidelity scenario synthesis and real-time testing, showcasing significant potential in editing and testing within complex environments. As technology advances, the safety of autonomous driving will be increasingly assured.