Driving risk assessment under the connected vehicle environment:a CNN-LSTM modeling approach

Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.