控制与信息技术Issue(2)：95-102,8.DOI:10.13889/j.issn.2096-5427.2026.02.300
基于跟踪时序的多阶段目标融合算法
Multi-Stage Object Fusion Algorithm Based on Tracking Time Series
摘要
Abstract
This paper presents a multi-stage object fusion strategy based on tracking time series to enhance object perception based on multi-sensor fusion in rail transit scenarios.Individual-sensor object detection and tracking are first carried out using camera and LiDAR;a multi-stage object fusion algorithm is then used based on tracking time series data to address omissions from the individual detection stage,thus increasing object perception performance.The algorithm employs a joint similarity calculation approach based on point-box displacement and the normalized Gaussian Wasserstein distance(NWD)to improve fusion accuracy.Additionally,the incorporation of an expanded Kuhn-Munkres(KM)algorithm enables matching a single visual detection to LiDAR clustering to further increase fusion accuracy.Experiments with perception equipment installed on a train show that this method produces real-time fusion results for objects ahead of the train,achieving fusion accuracies up to 95.1%for trains and 77.7%for pedestrians,with an average time consumption of 34.79 ms,providing reliable warnings to enhance active safety for trains.关键词
轨道交通/自动驾驶/目标感知/多传感融合/相机/激光雷达Key words
rail transit/autonomous driving/object perception/multi-sensor fusion/camera/LiDAR分类
信息技术与安全科学引用本文复制引用
苏铭,蔡毅,黄子仪,李晨,袁希文,李程..基于跟踪时序的多阶段目标融合算法[J].控制与信息技术,2026,(2):95-102,8.基金项目
国家重点研发计划项目(2022YFB4300602) （2022YFB4300602）
