为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNL...为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNLSTM网络的车辆换道意图识别模型。首先,将目标车辆的驾驶意图分为直行、向左换道和向右换道3种类型,从CitySim车辆轨迹数据集中提取出目标车辆及对应同车道、左侧车道、右侧车道的相邻前车和相邻后车的轨迹数据,并利用中值滤波算法获得车辆运行状态指标。其次,针对统计学理论和机器学习方法面临的识别精度不高、训练时间长、参数更新慢等问题,提出利用膨胀卷积技术提取时间序列的时序特征,采用门控记忆单元捕捉时序特征的长期依赖关系,并以目标车辆及周边相邻车辆的速度、加速度、航向角、航向角变化率和相对位置信息等54个车辆状态指标为输入变量,以车辆的换道意图为输出变量,构建了一个基于TCN-LSTM网络的车辆换道意图识别模型。最后,对比分析了不同输入时间步长下TCN、支持向量机(Support Vector Machines,SVM)、LSTM和TCN-LSTM模型的识别精度。结果表明:输入时间序列长度为150帧时,TCN-LSTM模型的识别精度达到最高值96.67%;从整体分类精度来看,相比LSTM、TCN和SVM模型,TCN-LSTM模型的换道意图分类准确率分别提升了1.34、0.84和2.46个百分点,展现出了更高的分类性能。展开更多
To examine the influence of the harsh environment in plateau areas on the operating speed of vehicles,advanced speed prediction models for curved segments are established based on observed actual speed data.First,the ...To examine the influence of the harsh environment in plateau areas on the operating speed of vehicles,advanced speed prediction models for curved segments are established based on observed actual speed data.First,the speed characteristics at the starting,mid,and end points of a plane curve were observed on Lalin Highway and China National Highway 318 with Bushnell s handheld radar speedometer 10-1911CN.Second,the stepwise regression method was proposed to determine the significant parameters and propose the prediction models of the operating speed of cars and large vehicles for the two highways.Finally,reserved test group data were utilized to prove the validity and practicality of the proposed models.Compared with traditional methods,the established models can produce more accurate prediction results and deeply examine the nonlinear relationships between parameters and the predicted operating speed.This study provides a considerate direction and basis for the operating speed prediction model for other segments in plateau regions.展开更多
The biological species concept defines species in terms of interbreeding. Interbreeding between spe-cies is prevented by reproductive isolation mechanisms. Based on our results of interspecific hybridi-zation between ...The biological species concept defines species in terms of interbreeding. Interbreeding between spe-cies is prevented by reproductive isolation mechanisms. Based on our results of interspecific hybridi-zation between Helicoverpa armigera and Helicoverpa assulta, reproductive isolation mechanisms of the two species are analyzed. A combination of prezygotic factors (absent sex attraction and physical incompatibility of the genitalia) and postzygotic factors (female absence and partial sterility in F1 hy-brids) causes reproductive isolation of the two species. In addition, the role of interspecific hybridiza-tion in speciation is discussed.展开更多
文摘为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNLSTM网络的车辆换道意图识别模型。首先,将目标车辆的驾驶意图分为直行、向左换道和向右换道3种类型,从CitySim车辆轨迹数据集中提取出目标车辆及对应同车道、左侧车道、右侧车道的相邻前车和相邻后车的轨迹数据,并利用中值滤波算法获得车辆运行状态指标。其次,针对统计学理论和机器学习方法面临的识别精度不高、训练时间长、参数更新慢等问题,提出利用膨胀卷积技术提取时间序列的时序特征,采用门控记忆单元捕捉时序特征的长期依赖关系,并以目标车辆及周边相邻车辆的速度、加速度、航向角、航向角变化率和相对位置信息等54个车辆状态指标为输入变量,以车辆的换道意图为输出变量,构建了一个基于TCN-LSTM网络的车辆换道意图识别模型。最后,对比分析了不同输入时间步长下TCN、支持向量机(Support Vector Machines,SVM)、LSTM和TCN-LSTM模型的识别精度。结果表明:输入时间序列长度为150帧时,TCN-LSTM模型的识别精度达到最高值96.67%;从整体分类精度来看,相比LSTM、TCN和SVM模型,TCN-LSTM模型的换道意图分类准确率分别提升了1.34、0.84和2.46个百分点,展现出了更高的分类性能。
基金The National Natural Science Foundation of China (No. 51768063, 51868068)Shanxi Provincial Innovation Center Project for Digital Road Design Technology (No. 202104010911019)。
基金The National Natural Science Foundation of China(No.51768063,51868068)。
文摘To examine the influence of the harsh environment in plateau areas on the operating speed of vehicles,advanced speed prediction models for curved segments are established based on observed actual speed data.First,the speed characteristics at the starting,mid,and end points of a plane curve were observed on Lalin Highway and China National Highway 318 with Bushnell s handheld radar speedometer 10-1911CN.Second,the stepwise regression method was proposed to determine the significant parameters and propose the prediction models of the operating speed of cars and large vehicles for the two highways.Finally,reserved test group data were utilized to prove the validity and practicality of the proposed models.Compared with traditional methods,the established models can produce more accurate prediction results and deeply examine the nonlinear relationships between parameters and the predicted operating speed.This study provides a considerate direction and basis for the operating speed prediction model for other segments in plateau regions.
基金the National Natural Science Foundation of China (Grant No. 30330100)the CAS Innovative Research International Partnership Project (Grant No. CXTDS2005-4)
文摘The biological species concept defines species in terms of interbreeding. Interbreeding between spe-cies is prevented by reproductive isolation mechanisms. Based on our results of interspecific hybridi-zation between Helicoverpa armigera and Helicoverpa assulta, reproductive isolation mechanisms of the two species are analyzed. A combination of prezygotic factors (absent sex attraction and physical incompatibility of the genitalia) and postzygotic factors (female absence and partial sterility in F1 hy-brids) causes reproductive isolation of the two species. In addition, the role of interspecific hybridiza-tion in speciation is discussed.
