自动生成全球范围的自动驾驶安全事件数据，解决真实测试难、仿真差距大问题。
TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
- 从任意地理位置自动获取地图与交通需求，模拟自然驾驶行为并制造极端场景。
- 结合街景图像生成逼真传感器数据，实现地理精准的视觉渲染。
- 适合需要海量安全场景训练与评估的自动驾驶研发团队使用。
端到端自动驾驶的安全可扩展部署需大量多样化的安全关键数据。现有数据主要来自存在显著仿真到现实差距的模拟器，或成本高且不安全的道路实测。本文提出TeraSim-World，一个自动化管道，可在全球任意位置合成真实且地理多样化的安全关键数据。该方法从任意地点获取真实地图与交通需求，基于自然驾驶数据模拟智能体行为，并编排多种异常情况以生成边缘案例。借助同一地点的街景信息，通过前沿视频生成模型Cosmos-Drive实现照片级真实感、地理对齐的传感器渲染。通过融合代理与传感器仿真，TeraSim-World为端到端自动驾驶系统的训练与评估提供可扩展的关键数据合成框架。代码与视频见https://wjiawei.com/terasim-world-web/。
原文摘要 · Abstract (English)
Safe and scalable deployment of end-to-end (E2E) autonomous driving requires extensive and diverse data, particularly safety-critical events. Existing data are mostly generated from simulators with a significant sim-to-real gap or collected from on-road testing that is costly and unsafe. This paper presents TeraSim-World, an automated pipeline that synthesizes realistic and geographically diverse safety-critical data for E2E autonomous driving at anywhere in the world. Starting from an arbitrary location, TeraSim-World retrieves real-world maps and traffic demand from geospatial data sources. Then, it simulates agent behaviors from naturalistic driving datasets, and orchestrates diverse adversities to create corner cases. Informed by street views of the same location, it achieves photorealistic, geographically grounded sensor rendering via the frontier video generation model Cosmos-Drive. By bridging agent and sensor simulations, TeraSim-World provides a scalable and critical data synthesis framework for training and evaluation of E2E autonomous driving systems. Codes and videos are available at https://wjiawei.com/terasim-world-web/ .
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