Method for Identifying Dangerous Driving Behaviors in Heavy-duty Trucks Based on Multi-modal Data
This paper proposes a multi-modal method to identify dangerous driving behaviors of heavy-duty trucks,which integrates driving operation data,eye-tracking data and electrocardiogram(ECG)data in the analysis.A naturalistic driving experiment is designed to collect driving data using three types of devices:vehicle inertial navigation systems,eye-tracking decoders,and physiological data recorders.A multi-modal driving dataset is established through data synchronizing and data cleaning processes.The dangerous driving behaviors are divided into two categories:dangerous manipulation behaviors and fatigue driving behaviors.By extracting data features,nine dangerous driving behavior indicators are defined to represent five types of dangerous driving behaviors,including speeding,unstable speed,rapid speed changing,rapid lane changing,and fatigue driving.For dangerous manipulation behaviors,characteristic thresholds are determined through literature review,indicator calculation,and interquartile range method.For fatigue driving behaviors,fatigue driving levels are identified through factor analysis and K-means clustering methods.A random forest(RF)classification model is then developed to identify dangerous driving behavior.When compared to traditional methods,including back propagation neural network(BPNN),K-nearest neighbors(KNN),support vector machine(SVM),the proposed model surpassed others in terms of accuracy and fitting performance.The model achieved a classification accuracy of over 90%for all types of dangerous driving behaviors.The results prove that the proposed methods are effective in identifying dangerous driving behaviors and it provides a theoretical basis for multimodal warning systems of dangerous driving behaviors.
NETL
NSTL
万方数据
维普
