Quantitative Study Of Cognitive Load On The Combination Of Grassland Highway Intersection Facilities

As the key control node of the road and the intersection of traffic flow,the intersection has a complex driving environment,variable vehicle driving lines,and many conflict points.At the same time,the grassland highway has a single alignment,a monotonous roadside landscape,little topographic and geomorphological changes,and low driver operation conversion frequency,which can easily cause driver’s mental fatigue,resulting in driver’s lack of concentration and reduced ability to process information.Combined with the preliminary investigation,it is found that the number of small plane intersections of grassland highways is large,the spacing is dense,the traffic facilities are unreasonable and the continuity is poor,and it is difficult for drivers to make rapid and accurate judgments,resulting in intersections becoming key risk areas for grassland highway traffic safety.Therefore,studying the influence of the combination of traffic engineering facilities at grassland highway intersections on driver’s cognitive load is of great significance to deeply explore the cognitive working mechanism and load capacity of the driver’s brain,so as to improve the rationality and safety level of intersection traffic facilities,and promote the deep integration of human-machine systems on grassland highways.Based on this,according to the current situation and existing problems of the setting of traffic engineering facilities at grassland highway intersections,simulation experiments are carried out,typical grassland highway intersection scenarios are built,traffic engineering facilities with different information levels are set up,and driver’s EEG signals and reaction time are collected.The influence of different combinations on the brain state of drivers was analyzed,the EEG microstate characteristic indicators of driver cognitive processing transportation facilities were explored,and a quantitative model of driver’s cognitive load was constructed by combining the entropy weight method.The main findings of this thesis are as follows:(1)The investigation and statistics of grassland highway intersections found that the setting of traffic engineering facilities in the grassland highway intersection area was missing,the amount of information was unreasonable,the characteristics of grassland roads and the interaction between traffic facilities were not fully considered,there was a lack of continuity,and the guidance system of traffic facilities before the intersection was not perfect.(2)The combination of intersection traffic engineering facilities was studied as a whole,the changes of the driver’s brain state caused by the combination of different types of traffic facilities were analyzed,and the EEG signals were fitted into five types of microstate topographic maps,and it was found that microstates MS2 and MS4 always dominated the combined cognitive tasks,and the default mode network and dorsal attention network played the main role in the cognitive process of the combination of intersection facilities.(3)Combined with the analysis of micro-state indicators and driver reaction time,it is found that when there are four types of traffic engineering facilities included in the visual combination combination,the driver’s brain function network has the best performance and high operational efficiency,which is suitable for the combination of grassland highway intersection traffic facilities.(4)Statistical analysis and trend comparison of microstate index duration,coverage,frequency of occurrence and conversion probability showed that the duration of MS4 and the conversion probability of MS1 to MS3 had significant differences between different information levels(P <0.05),which can be used as a direct basis index for the change of driver’s cognitive load,and shows an upward trend with the increase of traffic facilities,which is positively correlated with driver’s cognitive load.Cov1,Cov5,Dur2 and Occ1 showed a downward trend with the increase of information in the combination of transportation facilities,which were negative indicators of driver’s cognitive load.Comprehensively considering the characteristic meaning and trend changes of each index,the driver cognitive load index system was constructed.(5)Combined with the entropy weight method,a cognitive load quantification model based on the driver microstate index system was constructed,and the effectiveness of the model was verified by combining the information of transportation facilities,using the NASA-TLM subjective load quantification table and reaction time.