Fusion Innovation of Collaborative Perception and Large Models in Intelligent Transportation
协同感知与大模型在智慧交通中的融合创新
Chair: | Co-chairs: | |
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Jianqing Wu | Mengmeng Zhang | Yuan Tian |
Shandong University, China | Shandong Jiaotong University | Shandong University, China |
Keywords: | Topics: |
· Collaborative Perception (协同感知) · Large-Scale Models (大模型) · Vehicle-Road Collaboration (车路协同) · Multi-Source Data Fusion (多源数据融合) · Intelligent O&M (智能运维) | · Vehicle-Road Collaborative Perception (车路协同信息感知) · Multi-Source Traffic Data Fusion (多源交通数据融合) · Intelligent Traffic Conflict Management (交通冲突智能管理) · Generative AI for Traffic Simulation (生成式AI在交通仿真中的应用) · Architecting & Fine-Tuning Traffic Foundation Models (交通大模型构建与微调) · Real-Time Decision Optimization via Large AI Models (大模型驱动的实时决策优化) · Performance Evaluation of Green Intelligent Transportation Systems (绿色智能交通效能评估) · Intelligent Detection of Structural Diseases in Transport Infrastructure (交通基础设施病害智能检测) |
Summary:
· In recent years, collaborative perception and large AI models have reshaped smart transportation through groundbreaking innovations in both theory and practice. Advances in multi-sensor fusion and data-driven operation, accelerated by national funding (e.g., MOT, NSFC), position China at the global forefront.
To capture these developments, we present: "Fusion Innovation of Collaborative Perception and Large Models in Smart Transportation".
This session will:
Review advances in vehicle-road collaborative sensing and large-model-based decision systems;
Analyze frontiers: from multi-source data fusion to generative AI for traffic optimization;
Forecast next-gen trends for AI-native transportation ecosystems.
· 近年来，协同感知与AI大模型技术正在重塑智慧交通领域，推动理论与工程实践的革命性突破。 在人工智能背景下，多源信息融合理论与数据驱动型交通运维范式快速发展。依托交通运输部、国家自然科学基金委等国家级项目支持，我国在智能交通领域取得系列前沿成果，确立了全球技术引领地位。
为系统总结上述进展，特设立专题：《协同感知与大模型在智慧交通中的融合创新》
本专题旨在：
梳理协同感知与大模型决策系统的理论突破；
解析车路协同感知、多模态数据融合、基础设施智能诊断等前沿成果；
展望大模型赋能的下一代智慧交通系统发展趋势。
Submission Deadline: 2025/8/30