Elevation Aware 2D/3D Co-simulation Framework for Large-scale Traffic Flow and High-fidelity Vehicle Dynamics

2025-12-11

AI Summary

Existing autonomous driving simulators commonly neglect real-world terrain elevation, limiting their capability to support closed-loop testing in mountainous urban environments. To address this, we propose the first elevation-aware 2D/3D co-simulation framework integrating SUMO and CARLA, leveraging OpenStreetMap road networks and USGS elevation data for end-to-end automated 3D city modeling. We introduce a curvature-constrained smooth interpolation algorithm to ensure physical consistency of terrain geometry, and design a cross-platform time-synchronization interface with geometric validation to guarantee simulation fidelity. Evaluated across multiple districts in San Francisco, our framework successfully reconstructs steep slopes and irregular topography; under large-scale simulations involving thousands of vehicles, elevation error remains below 0.3 m. This work establishes the first fully automated pipeline for real-world terrain modeling and verification in multi-platform co-simulation, overcoming a critical limitation of conventional simulators—namely, the omission of complex topographic features.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

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.

Problem

• Integrates large-scale traffic flow with high-fidelity vehicle dynamics simulation
• Captures real-world elevation data for realistic 3D urban environments
• Enables reliable autonomous driving testing in complex topographic cities

Innovation

• Integrates SUMO and CARLA via automated pipeline
• Fuses OpenStreetMap and USGS elevation data
• Generates synchronized 2D-3D simulation with terrain validation