南京师大学报（自然科学版）2026，Vol.49Issue(2)：85-97,13.DOI:10.3969/j.issn.1001-4616.2026.02.009
基于时空交互信息融合的车辆违规超车识别
Spatiotemporal Interaction Information Fusion for Vehicle Illegal Overtaking Recognition
摘要
Abstract
To improve the accuracy of vehicle illegal overtaking recognition,this paper proposes spatiotemporal interaction information fusion for vehicle illegal overtaking recognition algorithm.The algorithm is built upon the TimeSformer architecture as the backbone model.Four types of modality information,namely RGB images,optical flow,depth maps,and overtaking interaction graphs,are integrated to construct a unified overtaking information graph.From multiple perspectives,including appearance features,motion information,3D spatial structure,and inter-vehicle interaction relationships,the method performs joint modeling of overtaking behaviors.By introducing divided space-time attention mechanism and multi-modal feature fusion strategy,the proposed approach effectively captures the dynamic evolution of the target vehicle during the overtaking process as well as its spatiotemporal interactions with surrounding vehicles,thereby alleviating the insufficient representation of multi-vehicle interactions in complex traffic scenarios.Experimental results on the PREVENTION dataset show that the proposed algorithm achieves a recognition accuracy of 94.04%for illegal overtaking behaviors,outperforming several existing mainstream algorithms and validating the effectiveness of multimodal spatiotemporal interaction information fusion for complex traffic behavior recognition.关键词
智能交通/车辆违规超车行为识别/时空交互信息融合/多车辆交互建模/TimeSformerKey words
intelligent transportation/vehicle illegal overtaking recognition/spatiotemporal interaction information fusion/multi-vehicle interaction modeling/TimeSformer分类
交通工程引用本文复制引用
巢新,吉根林,赵斌,麦丞程,王嘉琦..基于时空交互信息融合的车辆违规超车识别[J].南京师大学报（自然科学版）,2026,49(2):85-97,13.基金项目
国家自然科学基金项目(41971343)、江苏省前沿技术研发计划项目(BF2024005). （41971343）
