CDA-SimBoost: A Unified Framework Bridging Real Data and Simulation for Infrastructure-Based CDA Systems

TL;DR

CDA-SimBoost is a comprehensive framework that integrates real-world data and simulation to enhance infrastructure-based cooperative driving automation research, addressing long-tail challenges and sensor heterogeneity.

Contribution

It introduces a modular, unified platform combining data processing, high-fidelity simulation, and benchmarking for infrastructure-centric CDA development.

Findings

Supports realistic scenario creation and rare event synthesis.

Enables scalable evaluation and reproducibility in CDA research.

Bridges real data and simulation for comprehensive CDA studies.

Abstract

Cooperative Driving Automation (CDA) has garnered increasing research attention, yet the role of intelligent infrastructure remains insufficiently explored. Existing solutions offer limited support for addressing long-tail challenges, real-synthetic data fusion, and heterogeneous sensor management. This paper introduces CDA-SimBoost, a unified framework that constructs infrastructure-centric simulation environments from real-world data. CDA-SimBoost consists of three main components: a Digital Twin Builder for generating high-fidelity simulator assets based on sensor and HD map data, OFDataPip for processing both online and offline data streams, and OpenCDA-InfraX, a high-fidelity platform for infrastructure-focused simulation. The system supports realistic scenario construction, rare event synthesis, and scalable evaluation for CDA research. With its modular architecture and standardized benchmarking capabilities, CDA-SimBoost bridges real-world dynamics and virtual environments, facilitating reproducible and extensible infrastructure-driven CDA studies. All resources are publicly available at https://github.com/zhz03/CDA-SimBoost