Research on driving behavior analysis and evaluation model based on truck driving data
Running data of heavy trucks of 60 000 km are collected,and a driving economy and safety evaluation method based on mechanism analysis and data feature fusion is proposed.First,through the processing of actual operating data,six driving processes of the vehicle with the same external environment are selected through the control variables,and three characteristic parameters closely related to the instantaneous fuel consumption are extracted through the mechanism model analysis,which are clustered into three fuel consumption levels.Then,an economic evaluation model with instantaneous fuel consumption prediction as the main objective is established.After training and testing,the prediction accuracy of the model reaches 96.3％.From the perspective of longitudinal control analysis,the three vehicle driving safety indicators are calculated,and the driving safety level of each vehicle is classified.The multi-dimensional hierarchical analysis method is used to calculate their respective weights,so as to evaluate the safety of vehicle driving behavior.Finally,the results of the evaluated model are verified by a case.
big data of vehicle internetdriving behaviorevaluation modelcase validation
NETL
NSTL
万方数据
