考虑客货交互压迫的小汽车换道风险预测研究  

Crash Risk Prediction for Car Lane Changing Behavior Considering Oppression in Car-truck Interactions

作　　者：邢璐 唐幼仪 裴欣 王宝杰[4] 曹一君 姚丹亚[2] XING Lu;TANG You-yi;PEI Xin;WANG Bao-jie;CAO Yi-jun;YAO Dan-ya(School of Traffic and Transportation Engineering,Changsha University of Science&Technology,Changsha 410114,Hunan,China;Department of Automation,Tsinghua University,Beijing 100084,China;Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems,Changsha University of Science&Technology,Changsha 410114,Hunan,China;School of Transportation Engineering,Chang'an University,Xi'an 710064,Shaanxi,China)

机构地区：[1]长沙理工大学交通运输工程学院,湖南长沙410114 [2]清华大学自动化系,北京100084 [3]长沙理工大学智能道路与车路协同湖南省重点实验室,湖南长沙410114 [4]长安大学运输工程学院,陕西西安710064

出　　处：《中国公路学报》2025年第3期97-112,共16页China Journal of Highway and Transport

基　　金：国家自然科学基金青年科学基金项目(52102405,52102407);国家重点研发计划项目(2021YFC3001500);湖南省“三尖”创新人才工程项目(2023RC3143);中国博士后科学基金项目(2023M731962);湖南省自然科学基金项目(2024JJ5030);长安大学中央高校基本科研业务费专项资金项目(300102343504);长沙理工大学研究生“实践创新与创业能力提升计划”项目(CLSJCX23010)。

摘　　要：高速公路客货混行场景下,客货车辆物理性能和驾驶行为差异易导致货车后方小汽车出现急加减速、超车等危险驾驶行为,影响交通流稳定性,加剧道路交通事故风险。鉴于此,聚焦于客货场景,创新性地以货车“压迫度”为切入点,探究客货车辆交互影响下小汽车的换道行为和行车风险。首先,引入分子相互作用力构建考虑驾驶风格的货车压迫度(Oppression Measurement of Truck,OMT)量化指标,以量化货车对小汽车行驶存在的压迫影响。进一步采用货车压迫度优化小汽车换道意图判别方法,并融合货车压迫度构建基于卷积神经网络-长短期记忆混合神经网络(CNN-LSTM)的换道意图识别模型和基于轻量级梯度提升机(LightGBM)的换道风险预测模型联合组成的两阶段换道风险预测模型,并采用车辆轨迹数据集验证模型的有效性。研究结果表明:有换道行为的车辆总体上受到更强烈的货车压迫;熟练型驾驶人能够容忍高强度的货车压迫,而谨慎型的驾驶人对货车压迫较为敏感,偏向于保持在低压迫度下行驶。其次,货车压迫度与行车冲突之间存在时间滞后相关效应,较强压迫度能影响车辆驾驶行为,进而引发行车风险变化。融合了货车压迫度指标的模型在换道意图识别与风险预测中表现出更高的精度,并且货车压迫度在换道风险预测模型中具有较高的特征贡献度,研究结果可为复杂交互场景的微观建模与主动安全管控提供全新视角和有效理论支撑。Under mixed traffic of cars and trucks,differences in physical performance and driving behavior between cars and trucks can easily lead to dangerous driving behaviors,such as the sudden acceleration,deceleration,or overtaking of cars,which can affect the stability of traffic flow and increase the risk of traffic accidents.Therefore,this study focuses on car-truck mixed traffic scenarios,innovatively proposes the“Oppression of Truck”concept,and explores the lane changing behaviors and driving risks of cars under the influences of car-truck interactions.First,the oppression measurement of trucks(OMT),considering driving style,was constructed by introducing molecular interaction forces to quantify the oppression of trucks on cars.Then,using the truck oppression measurement to optimize the car lane change intention identification method,a two-stage lane change crash risk prediction model was developed by integrating the OMT,which comprised a lane-change intention identification model based on a CNN-LSTM(Convolutional Neural Network-Long Short-Term Memory)hybrid neural network and a crash risk prediction model for car lane changing behavior based on LightGBM.A vehicle trajectory data set consisting of data from real traffic scenarios was used to verify the validity of the model.The results indicate that lane changing cars are generally more strongly oppressed by trucks.Moreover,skilled drivers can withstand high truck pressure,whereas cautious drivers are more sensitive to truck oppression and tend to keep driving under low pressure.Additionally,there is a time-lag correlation between truck oppression and driving risk,where stronger oppression can affect vehicle driving behavior and lead to changes in driving risk.Models that incorporate the truck oppression indicator show higher accuracy in lane change intention identification and crash risk prediction.Truck oppression has a higher feature contribution in the crash risk prediction model,which provides a new perspective and effective theoretical support for the mi

关 键 词：交通工程 客货混行 货车压迫度 换道风险 换道意图 交通安全 

分 类 号：U491.25[交通运输工程—交通运输规划与管理]