Research On Driving Behavior Identification And Modeling Method Based On Natural Driving Data

As an important part of the development path of intelligent driving and intelligent traffic system,driving behavior has been paid much attention and research.By studying the driving behavior under the safe driving scene,we can understand the driving condition of the vehicle in real time,and then improve the driving safety effectively.By studying the reasonableness of driving behavior under the accident scene can reduce the dispute and speed up the accident treatment.In the safe driving scene,accurately estimating the end time for follow-up behavior and lane-change behavior of vehicle is conducive to early warning of vehicles to ensure driving safety.At the same time,the research on improving driving safety based on accident data is also increasing.Research on the rationality of driving behavior in accident scenario can reduce the dispute and improve the accident handling and driving efficiency.In view of the above problems,this paper designs a model to estimate the survival time of driving behavior based on the survival analysis method,the algorithm model that predicts vehicle intention and trajectory by using the deep learning algorithm Gated Recurrent Unit(GRU),and the model of driving behavior evaluation under the rear-end accident Hidden Markov Model(HMM)is proposed.The main research content of this paper includes:(1)A method for estimating the survival time of driving behavior based on the Survival Analysis model is designed.Under the safe driving scene,the vehicle trajectory information in the NGSIM dataset of the naturalistic driving data set is used to analyze the driving behavior of the two basic driving behaviors of the follow-up vehicle and the lane change.The survival analysis model is introduced to estimate the probability distribution of the survival time with the vehicle and the change of behavior,and then determine the behavior deadline to enhance the safety of the driving.(2)A vehicle intent detection and trajectory prediction model based on GRU algorithm is designed.Under the safe driving scene,according to the trajectory information of vehicle in the NGSIM dataset of the naturalistic driving data set,the characteristics of vehicle dynamics are analyzed,the characteristics of the vehicle are extracted,such as lateral speed and lateral acceleration of vehicle,and the vehicle trajectory prediction model of the fusion driving intention is constructed using the GRU of deep learning method,so as to make a more accurate prediction and classification of driving behavior.(3)A driving behavior evaluation strategy under the scene of a rear-end accident is proposed.Under the accident scene,the rear-end accident forensic system based on multi-edge calculation is constructed,the driving behavior is analyzed by using the 100-Car dataset,and the driving behavior characteristics under the rear-end accident scene are obtained,it is attributed to the trajectory characteristics of the vehicle,the possible safe driving behavior parameters can be decoded by the HMM,and the reasonable liability distribution mechanism should be developed with the characteristics of the rear-end accident scene,with a view to reduce the dispute to speed up the handling of accidents and driving efficiency.