Zheng, Zhaoliang (UCLA), Han, Xu (University of California, Los Angeles), Bao, Yuxin (University of California, Los Angeles), Zhang, Yun (University of California, Los Angeles), Liu, Johnson (University of California, Los Angeles), Meng, Zonglin (University of California, Los Angeles), Xia, Xin (University of Michigan, Dearborn), Ma, Jiaqi (University of California, Los Angeles)

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

Scheduled for presentation during the Invited Session "S19b-Artificial Transportation Systems and Simulation" (TH-EA-T19), Thursday, November 20, 2025, 14:50−14:50, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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.