基于自組織K-means的城市道路VRU事故場景復(fù)雜度評價

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關(guān)鍵詞：弱勢道路使用者（VRU）；智能汽車；典型場景；自組織K-means聚類分析中圖分類號：U467 文獻標(biāo)識碼：A DOI：10.3969/j.issn.1674-8484.2025.03.004

Abstract：Inorder to address the requirements of high-risk testing environments forvalidating inteligent vehicle collsion avoidance systems，while simultaneously to enrich the content and methods for evaluating autonomous driving scenarios involving vulnerable road users （VRU）.This studycollectedand systematically analyzed trafficaccidentcases inGuilinCityGuangxiProvince，from2016 to2020.A totalof1429vehicle

VRUcollsionaccident data werescreened.Based onaccident investigationexperience，13 risk factors were identified，and10typical vehicle-VRUcollsionscenariosapplicable tourbantraffcconditionsinwere constructed using self-organizing K-meansclusteringanalysis.Anevaluation modelforthe complexityof VRU scenarios was established utilizing information entropy theory.The stateof variablesand theweightof each dimension were determined through a combination of logistic regression modelsand back propagation （BP） neural networks，and thecomplexityof various scenarios wascalculated.Aditionally，the Gaussianmixture model was employed tocluster thecomplexity levels，resulting in four distinct scenecomplexitycategories.The results show that on roads with a speed limit of 30km/h ，the nighttime side collsion betweena straight-moving carandanelectric bicycle crossing the road outsideapedestriancrossing areais the most complex scenario. Thefindings inthis study providean experimental scenarioreflectiveof urbanroadcharacteristics in for intelligent vehiclesafety testingandoffera basis for the formulationofexternalVRUcollsionavoidance strategies and decision-making.

Key words： vulnerable road users （VRU）; intelligent vehicles; typical hazardous scenarios; self-organizing; k-means clustering analysis

隨著自動駕駛技術(shù)的迅速發(fā)展，智能汽車正逐漸成為交通系統(tǒng)的重要組成部分。（剩余15357字）