北京交通大学学报2025，Vol.49Issue(5)：52-65,14.DOI:10.11860/j.issn.1673-0291.20250127
面向动态交通场景理解的多模态鲁棒学习综述
Review on multimodal robust learning for dynamic traffic scenario understanding
摘要
Abstract
Due to constantly interacting targets,rapidly shifting environments,and the inherent hetero-geneity of multi-sensor data,dynamic traffic scenarios impose stringent demands on the perceptual ro-bustness and decision reliability of intelligent systems.Multi-modal learning emerges as a critical solu-tion to overcome bottlenecks in dynamic scenario understanding by fusing heterogeneous modalities.This paper offers a systematic review on multimodal robust learning for dynamic traffic scenarios.First,we clarify the definition of multimodal dynamic traffic scenarios,and analyzes the types and dy-namic characteristics of multi-source modalities(e.g.,optical,radio frequency,and acoustic).Next,we lay out the fundamental principles of multi-modal learning,paying particular attention to key tech-niques that enhance robustness across data-level processing,model architectures,and training strate-gies.Furthermore,we delve into core challenges currently confronting the field and future research di-rections.The review highlights current challenging of data imperfections,model limitations,and the absence of evaluation benchmarks,and chart future directions toward three aspects:technological inno-vation,technology integration,and collaborative industry efforts.Our aim is to provide a systematic reference for both theoretical research and practical deployment of multimodal robust learning in dy-namic traffic scenarios,facilitate the evolution of intelligent transportation systems from being"avail-able in limited scenarios"to achieving"reliability across all conditions,"and provide critical technical support for the widespread adoption of intelligent transportation solutions.关键词
交通信息工程/动态交通场景/多模态数据/多模态学习/鲁棒学习Key words
traffic information engineering/dynamic traffic scenario understanding/multimodal data/multimodal learning/robust learning分类
信息技术与安全科学引用本文复制引用
李浥东,臧钊,章子凯,张旭,荣啸,李子毅..面向动态交通场景理解的多模态鲁棒学习综述[J].北京交通大学学报,2025,49(5):52-65,14.基金项目
国家自然科学基金(U2268203) （U2268203）
国家铁路集团科技研究开发计划项目(K25D00011)National Natural Science Foundation of China(U2268203) （K25D00011）
National Railway Group's Science and Technology Research and Development Program(K25D00011) （K25D00011）
