基于高斯混合-隱馬爾可夫模型的駕駛意圖識(shí)別

打開文本圖片集

主題詞：自動(dòng)駕駛 駕駛意圖識(shí)別高斯混合模型隱馬爾可夫模型Frenet坐標(biāo)系中圖分類號(hào)：U463.6 文獻(xiàn)標(biāo)志碼：A DOI：10.19620/j.cnki.1000-3703.20231201

Driving Intention Recognition Based on Gaussian Mixture-Hidden MarkovModel

Shen Yu122，Liu Guanghui2，Ma Xuanpeng1，XuJiawen2，Yan Yuan2 （1.School of Information Engineering，Gansu Minzu Normal University，Hezuo 747ooo；2.Schoolof Electronicand Information Engineering，Lanzhou Jiaotong University，Lanzhou ）

【Abstract】To achieve accurate recognition of vehicle driving intentions in highway scenarios，this paper proposes a drivingintentionrecognitionmodelthatcombinesdualreferencelinesintheFrenetcoordinatewith GausianMixture Models （GMMs）andHidden Markov Models （HMMs）.Themodel selectsdrivingdata fromdiferentreference linesintheFrenet coordinatebasedonvehiclepositionasobservedvariables.By integratingtheobservationprobabilitiesoutputbytheGMMat previousand subsequent time steps withthe HMM，the model identifies the vehicles’driving intentionat thecurrentmoment. Theefectivenessof themodelisvalidatedusingtheUS-101datasetfrom NGSIM.Theresultsshow that thedual-referenceline GMM-HMM model achieves recognition accuracies of 93.33% for lane keeping and 92.24% for lane changing，indicating excellent recognition performance.

Key Words：Autonomous driving，Driving intention recognition，Gaussan Mixture Model （GMM），HiddenMarkovModel （HMM），F(xiàn)renetcoordinate

【引用格式】沈瑜，劉廣輝，馬翱鵬，等.基于高斯混合-隱馬爾可夫模型的駕駛意圖識(shí)別[J].汽車技術(shù)，2025（5）：22-28SHENY，LIUGH，MAXP，etal.Driving IntentionRecognitionBasedonGausian Mixture-HiddenMarkovModel[J].Automobile Technology，2025（5）：22-28.

1前言

道路交通事故原因分析表明，約四分之一的安全事故源于駕駛意圖傳達(dá)不明確[1。（剩余9483字）