Quantitative Research Methods Of Drivers’ Risk Perception Ability In Different Scenarios During Automated Driving

Automatic vehicles gradually enter our field of vision.Research on automated driving drivers in risk scenarios is urgent.When automatic vehicles face risk scenarios,the driver is responsible for monitoring the traffic environment.It will experience the three parts of identifying risk,judging risk and deciding to take-over,and the driver’s ability to perceive risk is closely related to these three steps.Therefore,this study designed a risk scenario driver take-over willingness scale,explored the impact of the driver’s risk perception in different risk scenarios,designed an automated driving experiment in different risk scenarios,and explored the physiological and eye movement characteristics of the driver in different risk scenarios.Finally put forward a quantitative method of risk perception.The summaries are as follows:(1)The development of driver willingness to take-over in risk scenario scale and model establishment.The scale is designed based on the external weather environment,traffic environment,and the driver’s positive state and message sentiment.A random questionnaire survey on 285 drivers was made in Hefei City,and the statistical results of the questionnaire scale were analyzed to verify the effectiveness of the risk-taking driver’s willingness to take-over the scale,and the statistical data were further analyzed,respectively 4 factors establish the path model.The results show that the risk scenario under the weather environment and traffic environment will be affected by the risk perception sub-factors,self-efficacy,perceived usefulness and perceived ease of use.(2)Automated driving simulation experiment design in collaboration with real car and simulator.Comprehensively considering the simulation effect and safety factors,a 10.8 km section of road with less traffic is selected in Hefei for recording of simulated scenes,and video files of risk scenes are obtained through real road recording,then the video files are imported into the driving simulator to complete the simulation automated driving risk scenario construction.The experiment selected 30 test drivers,and carried out the experiment after wearing the eye tracker and physiological instrument.After the experiment,the driver filled out the relevant questionnaire.(3)Data analysis of drivers’ physiological and psychological characteristics in different risk scenarios during autonomous driving.The experimental data obtained from the automatic driving simulation experiment.According to the physiological and eye movement classifications,analyzing the heart rate,RR interval,pupil diameter,average saccade number and gaze duration indicators respectively.To sum up in conclusion comparing the characteristics of drivers in risk-free scenarios and risk scenarios change and analyzing the impact of the change in the risk value of the scene on the driver.(4)The risk value prediction model of different risk scenarios and the quantitative method of the driver’s risk perception ability during automatic driving.Based on the multiple linear regression equation model to explore the relationship between pupil diameter and fixation duration and scene risk,establishing a risk scenario value prediction method including driving subjective assessment of scene risk,mean pupil diameter and fixation duration.Meantime,establishing five indicators through the gray near-optimal comprehensive evaluation model,the quantitative method of the driver’s risk perception ability,and making in-depth analysis of the quantitative value of the driver’s risk perception ability in 8 risk scenarios in this experiment.