Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior d...Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.展开更多
Perchlorate concentrations in 387 unhusked rice samples from 15 main producing provinces/municipality in China were investigated by high-performance liquid chromatography-tanden mass spectrometry.The results indicated...Perchlorate concentrations in 387 unhusked rice samples from 15 main producing provinces/municipality in China were investigated by high-performance liquid chromatography-tanden mass spectrometry.The results indicated that perchlorate displays a mean level of 17.17μg/kg in un-husked rice samples.Intriguingly,we also found that perchlorate is mainly observed in rice husk among these collected unhusked rice samples,while less observed in rice bran and milled rice.Specifically,the perchlorate levels in rice were found in the husks(73.61%),bran(10.09%),and milled rice(19.52%),respectively.Our results indicated that there is no significantly perchlorate exposure risk in edible milled rice.展开更多
基金financially supported by the National Key Research and Development Program of China(No.2021YFA1201500)the National Natural Science Foundation of China(No.22027802,No.22222308)+2 种基金the CAS project for Young Scientists and Basic Research(No.YSBR-007)the Natural Science Foundation of Shandong Province(No.ZR2021LLZ003)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB33000000).
基金This research was funded by the National Nature Science Foundation of China(No.52072290)Hubei Province Science Fund for Distinguished Young Scholars(No.2020CFA081)the Fundamental Research Funds for the Central Universities(No.191044003,No.2020-YB-028).
文摘Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.
基金the National Key Research and Development Program of China(2017YFC1600500)the Grain Industry Research Special Funds for Public Welfare Projects(No.201513006-03),China.
文摘Perchlorate concentrations in 387 unhusked rice samples from 15 main producing provinces/municipality in China were investigated by high-performance liquid chromatography-tanden mass spectrometry.The results indicated that perchlorate displays a mean level of 17.17μg/kg in un-husked rice samples.Intriguingly,we also found that perchlorate is mainly observed in rice husk among these collected unhusked rice samples,while less observed in rice bran and milled rice.Specifically,the perchlorate levels in rice were found in the husks(73.61%),bran(10.09%),and milled rice(19.52%),respectively.Our results indicated that there is no significantly perchlorate exposure risk in edible milled rice.
