Analysis Of Drivers’ Physiological Characteristics Under The Interaction Of Emotion And Behavior In Car-Following Driving

With the continuous progress of science,technology and economy,high-tech and cutting-edge technologies have been widely used in the transportation industry.There are many levels of traffic accidents caused by drivers and the proportion of accidents remains high.As one of the three key elements of participating in traffic,the driver’s physiology and behavior always affect the composition and change of traffic.Exploring the driver’s emotion and interaction mechanism has also become a breakthrough in the development of ITS.A deep understanding of the physiological characteristics of drivers in car-following driving can not only explore the differences in cognitive load during driving,but also provide a theoretical basis and reference basis for the anthropomorphic intelligent transportation system.Combined with the previous study,the multi-feature fusion method is used to analyze the physiological characteristics of drivers under the interaction of emotion and behavior,which provides new ideas and methods to study the traffic characteristics of drivers.To this end,this paper conducts linear and nonlinear research and significance comparative analysis of the physiological signals of drivers by mining their internal relations.The main research contents are as follows:(1)Develop emotional arousal experiments and simulated driving experiments in conjunction with the actual situation.In this paper,we select emotional stimulation materials and judgment scales in conjunction with the actual situation,and screen drivers through questionnaires and other means.A complete set of simulated driving experimental procedure is proposed to study the mechanism of driver emotional-behavioral interaction.The influence of three aspects of emotional arousal,traffic environment,and data accuracy on the experimental protocol setting was also considered to ensure the feasibility of the subsequent research content and the accuracy of the research findings.(2)Physiological signal pre-processing and extraction of feature parameters using various methods.In this paper,the physiological signal is noise-reduced by wavelet noise reduction and filter.On the basis of the pre-processed physiological signals,the time-domain,frequency-domain and nonlinear characteristic parameters of the three physiological signals are extracted according to their characteristics and principles and to analyze the differences.This provides data support for subsequent studies.(3)Analysis of physiological characteristics of drivers under the interaction of emotion and behavior.In this paper,BP neural network and DS evidence theory are combined.Multifeature fusion analysis of single physiological signals and fusion analysis of multiple physiological signals are performed to improve the accuracy of emotion recognition,respectively.The mechanism of driver emotion-behavior interaction is explored using emotions with higher recognition and more active signals.Determining similarities and differences in drivers’ physiological characteristics and differential cognitive load during interactions.This paper is based on the physiological characteristics of the driver under the interaction of emotion and behavior in car-following driving.Improving emotion recognition accuracy by feature fusion of different kinds of physiological signals.Analysis and investigation of the degree of influence and mechanism of emotion-behavior interaction using physiological characteristics parameters that differ significantly across emotions.It broadens the breadth of inquiry for exploring driving safety and provides theoretical support for personalized driving and assisted driving.