构建可扩展高保真自动驾驶仿真系统，支持复杂视角变化与空间一致性。
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
- 融合神经重建与生成建模，分别处理静态背景与动态车辆。
- 在多城市多路线数据集MIRROR上实现视觉与空间一致性突破。
- 适合自动驾驶算法研发与仿真平台建设者使用。
真实且可控的仿真对端到端自动驾驶的发展至关重要，但现有方法在大视角变化下的新视图合成和几何一致性方面仍存挑战。本文提出HybridWorldSim，一种混合仿真框架，将多遍历神经重建用于静态背景，结合生成建模处理动态代理。该统一设计克服了以往方法的关键缺陷，能够生成多样化且高保真的驾驶场景，确保视觉与空间一致性。为支持稳健评估，我们还发布了新的多遍历数据集MIRROR，覆盖多个城市的不同路线与环境条件。大量实验表明，HybridWorldSim超越现有最先进方法，在高保真仿真中提供实用且可扩展的解决方案，是自动驾驶研究与开发的重要资源。
原文摘要 · Abstract (English)
Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large viewpoint changes or to ensure geometric consistency. We introduce HybridWorldSim, a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents. This unified design addresses key limitations of previous methods, enabling the creation of diverse and high-fidelity driving scenarios with reliable visual and spatial consistency. To facilitate robust benchmarking, we further release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities. Extensive experiments demonstrate that HybridWorldSim surpasses previous state-of-the-art methods, providing a practical and scalable solution for high-fidelity simulation and a valuable resource for research and development in autonomous driving.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。