融合地形数据的2D/3D协同仿真，提升自动驾驶测试真实感
Elevation Aware 2D/3D Co-simulation Framework for Large-scale Traffic Flow and High-fidelity Vehicle Dynamics
- 用OpenStreetMap与USGS数据生成带高程的3D道路环境
- 在旧金山多区域验证，可精准还原陡坡与复杂地形
- 支持多平台同步仿真，适合城市复杂地形测试
自动驾驶系统可靠测试需要结合大规模交通建模与真实的3D感知和地形表现。现有工具普遍缺乏真实高程信息，限制了在复杂地形城市中的应用。本文提出一种自动化、高程感知的2D/3D协同仿真框架，通过集成SUMO与CARLA，利用道路网络（OpenStreetMap）和高程数据（USGS）生成物理一致的3D环境。系统生成平滑的高程剖面，验证几何精度，并实现跨平台的2D-3D同步仿真。在旧金山多个区域的演示表明，该框架具备可扩展性，能准确再现陡坡与不规则地形。结果为高保真自动驾驶测试提供了实用基础，适用于高程丰富的城市场景。
原文摘要 · Abstract (English)
Reliable testing of autonomous driving systems requires simulation environments that combine large-scale traffic modeling with realistic 3D perception and terrain. Existing tools rarely capture real-world elevation, limiting their usefulness in cities with complex topography. This paper presents an automated, elevation-aware co-simulation framework that integrates SUMO with CARLA using a pipeline that fuses OpenStreetMap road networks and USGS elevation data into physically consistent 3D environments. The system generates smooth elevation profiles, validates geometric accuracy, and enables synchronized 2D-3D simulation across platforms. Demonstrations on multiple regions of San Francisco show the framework's scalability and ability to reproduce steep and irregular terrain. The result is a practical foundation for high-fidelity autonomous vehicle testing in realistic, elevation-rich urban settings.
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