Organizers | 组织者
Yisheng An (安毅生)
Chair
Professor, School of Information Engineering, Chang'an University
长安大学信息工程学院，教授
Chen Mu (慕晨)
Co-Chair
Professor, School of Information Engineering, Chang'an University
长安大学信息工程学院，教授
Shumei Liu (刘树美)
Co-Chair
Associate Professor, School of Information Engineering, Chang'an University
长安大学信息工程学院，副教授
Bo Sun (孙柏)
Co-Chair
Lecturer, School of Information Engineering, Chang'an University
长安大学信息工程学院，讲师
Yaxian Wang (王雅娴)
Co-Chair
Lecturer, School of Information Engineering, Chang'an University
长安大学信息工程学院，讲师
Abstract | 论坛简介
This forum focuses on "Perception, Understanding, and Distributed Computing for Intelligent Driving Tasks," addressing critical challenges as autonomous driving systems move toward large-scale and complex applications. Currently, stand-alone vehicle intelligence faces bottlenecks in edge-case perception and global decision optimization. Distributed computing architectures, realized through Vehicle-Road-Cloud (VRC) collaboration, provide a new path toward safer and more efficient collective intelligence. The forum will explore real-time fusion and unified understanding of multi-node sensory data under distributed frameworks, as well as collaborative decision-making and resource scheduling. We aim to bring together experts from academia and industry to explore the technical evolution from centralized to distributed systems, the balance between communication and computing power, and the emerging ecosystem of intelligent driving applications.
本分论坛以“智能驾驶任务的感知理解与分布式计算”为主题，聚焦自动驾驶系统在走向规模化、复杂化应用过程中面临的关键挑战。当前，单车智能在感知的极限场景理解与全局决策优化上存在瓶颈，而通过车-路-云协同的分布式计算架构，为实现更安全、更高效的群体智能驾驶提供了全新路径。论坛将重点探讨如何在分布式框架下，实现多节点感知数据的实时融合与统一理解，以及在此基础上的协同决策与资源调度。我们旨在汇聚产学研各方智慧，共同探索从集中式到分布式演进的技术路线、通信与算力平衡的系统设计，以及由此催生的新型智能驾驶应用生态。
Topics | 主题范围
- Real-time fusion of multi-source heterogeneous perception data for V2X | 面向车路协同的多源异构感知数据实时融合技术
- Distributed perception model training and inference based on edge or cloud-edge-end architectures | 基于边缘计算或云边端架构的分布式感知模型训练与推理
- Collective perception and collaborative scene understanding in intelligent driving | 智能驾驶场景下的群体感知与协同场景理解
- V2V/V2I communication protocols and high-reliability, low-latency transmission | 车-车、车-路通信协议与高可靠低时延传输技术
- Collaborative trajectory planning and collective decision optimization under distributed frameworks | 分布式计算框架下的协同轨迹规划与群体决策优化
- Dynamic distributed task scheduling and computing resource management strategies | 动态分布式任务调度与计算资源管理策略
- Network security, data privacy, and system security architectures for distributed driving | 面向分布式驾驶的网络安全、数据隐私与系统安全架构
- Simulation-based testing, verification, and evaluation methods for distributed intelligent driving | 基于仿真的分布式智能驾驶系统测试验证与评估方法
- Application platform design for distributed driving in specific scenarios (e.g., smart logistics, regional shuttles) | 特定场景（如智慧物流、区域接驳）下的分布式驾驶应用平台设计