为研究车辆在换道前与车辆在正常行驶时跟驰行为的差异性,从NGSIM数据库中提取了快速路上的543个换道行为和870个非换道行为的跟驰事件并进行特征分析。使用曼-惠特尼U检验(Mann-Whitney U test)验证了换道前车辆的跟驰行为与正常行驶...为研究车辆在换道前与车辆在正常行驶时跟驰行为的差异性,从NGSIM数据库中提取了快速路上的543个换道行为和870个非换道行为的跟驰事件并进行特征分析。使用曼-惠特尼U检验(Mann-Whitney U test)验证了换道前车辆的跟驰行为与正常行驶的跟驰行为存在显著差异。选取跟驰车辆的车速和车头间距作为性能指标,其均方根百分比误差之和为目标函数,并将目标车道的前车速度纳入到智能驾驶员模型(IDM)中,构建换道准备智能驾驶员跟驰模型(BLC-IDM),利用遗传算法对BLC-IDM进行参数标定和效果验证。研究结果表明,传统的IDM不适用于换道前车辆的跟驰行为,改进后的BLC-IDM拟合精度提高了20%。BLC-IDM可以更加精准地描述车辆换道前的特殊跟驰行为。展开更多
Accurately predicting the motion trajectories of surrounding vehicles is crucial for the safety of autonomous vehicles.Realizing the trajectory prediction of multi-target vehicles depends not only on the historical tr...Accurately predicting the motion trajectories of surrounding vehicles is crucial for the safety of autonomous vehicles.Realizing the trajectory prediction of multi-target vehicles depends not only on the historical trajectories but also requires clarifying the dynamic spatial interactions between vehicles and the temporal relationships between trajectories of different time series.However,existing trajectory prediction methods do not adequately consider the coupling effects of spatial interactions and temporal relationships,resulting in insufficient accuracy for multi-target vehicles trajectory prediction in highly interactive scenarios.This paper proposes a planning-coupled multi-target vehicles trajectory prediction network(PCTP-Net)that contains encoding,feature fusion,and trajectory decoding modules for modeling coupled interactions based on time and space.Firstly,the encoding module employs a bidirectional long short-term memory(Bi-LSTM)network to encode historical and planning trajectories of different time series,which combines the planning information of the ego vehicle with the prediction process of multi-target vehicles to realize the coupled interaction modeling based on time and space.Secondly,the feature fusion module introduces a convolutional social pooling layer to analyze the impact of trajectories with different temporal features on the prediction and captures the dynamic spatial interactions between vehicles.Finally,the trajectory decoding module proposes a trajectory prediction decoder that incorporates driving behavior decisions to improve the trajectory prediction accuracy of multi-target vehicles under interaction.The experiments on the NGSIM dataset and different traffic scenarios show that the proposed method can achieve accurate trajectory prediction in traffic scenarios with dense and highly interactive vehicles.展开更多
文摘为研究车辆在换道前与车辆在正常行驶时跟驰行为的差异性,从NGSIM数据库中提取了快速路上的543个换道行为和870个非换道行为的跟驰事件并进行特征分析。使用曼-惠特尼U检验(Mann-Whitney U test)验证了换道前车辆的跟驰行为与正常行驶的跟驰行为存在显著差异。选取跟驰车辆的车速和车头间距作为性能指标,其均方根百分比误差之和为目标函数,并将目标车道的前车速度纳入到智能驾驶员模型(IDM)中,构建换道准备智能驾驶员跟驰模型(BLC-IDM),利用遗传算法对BLC-IDM进行参数标定和效果验证。研究结果表明,传统的IDM不适用于换道前车辆的跟驰行为,改进后的BLC-IDM拟合精度提高了20%。BLC-IDM可以更加精准地描述车辆换道前的特殊跟驰行为。
基金funded by the Fujian Province University Industry-Academic Cooperation Project(Grant Number 2021H6019)。
文摘Accurately predicting the motion trajectories of surrounding vehicles is crucial for the safety of autonomous vehicles.Realizing the trajectory prediction of multi-target vehicles depends not only on the historical trajectories but also requires clarifying the dynamic spatial interactions between vehicles and the temporal relationships between trajectories of different time series.However,existing trajectory prediction methods do not adequately consider the coupling effects of spatial interactions and temporal relationships,resulting in insufficient accuracy for multi-target vehicles trajectory prediction in highly interactive scenarios.This paper proposes a planning-coupled multi-target vehicles trajectory prediction network(PCTP-Net)that contains encoding,feature fusion,and trajectory decoding modules for modeling coupled interactions based on time and space.Firstly,the encoding module employs a bidirectional long short-term memory(Bi-LSTM)network to encode historical and planning trajectories of different time series,which combines the planning information of the ego vehicle with the prediction process of multi-target vehicles to realize the coupled interaction modeling based on time and space.Secondly,the feature fusion module introduces a convolutional social pooling layer to analyze the impact of trajectories with different temporal features on the prediction and captures the dynamic spatial interactions between vehicles.Finally,the trajectory decoding module proposes a trajectory prediction decoder that incorporates driving behavior decisions to improve the trajectory prediction accuracy of multi-target vehicles under interaction.The experiments on the NGSIM dataset and different traffic scenarios show that the proposed method can achieve accurate trajectory prediction in traffic scenarios with dense and highly interactive vehicles.
