Study On Laws Of Space Distance Cognition Variance On The Ground In Dynamic Environment

Because the human factor has an important role in road traffic safety and the driver correctly percept external environment and vehicle movement information is the basis of the corresponding driving behavior, a good way to assure system safety and coordination is to improve people's reliability and information cognition capability. In the multiple channels to percept information of driver information provided by visual sensation is more than 90%.The driver correctly aware external environment information especially the spatial distance is of great significance to decision-making on people's behavior and road infrastructure design. Apart from the driver's physical and psychological factors illumination and color and other environmental conditions may influence distance cognition results. This paper on the basis of the information processing and the physical and psychological theory research laws of distance cognition and variance in daytime, dusk and night through dynamic experiments. The main research contents are as follows:1.Systematic analyze dynamic visual information processing research of the driver at home and abroad and particularly concern sub-consciousness of space distance and other unsolved problems. On the basis of cognitive engineering and experimental psychology this paper design experiment programs of the different environments and research method of data disposition and model foundation combined with the road traffic system and vehicle operating characteristics.2.Statistically analyze the distribution of experimental data in daytime and obtain characteristics of the results from the sub-consciousness.Study laws of distance discriminating results to red and green obstacles change with velocity at different depths distance and establish a linear regression model and BP neural network model.Test and compare the differences between two colors obstacles and establish models of cognition difference variances in use of neural networks. To consider the impact of speed and spatial distance use the dual surface regression and neural network method to establish dynamic distance perception model for sub-space distance.3.In use of the results of static experiment cognition establish logarithmic regression model to study the laws of cognition distance change with the environmental illumination. Test difference between red and green obstacles and utilize neural networks discriminating differences variance models in consideration of characteristics of visual recognition at dusk. Establish neural network models about cognition distance change with velocity and space distance through two input layer neurons network.4. According to drivers'recognition results at night establish negative exponential function model to describe cognition distance change with velocity. Test significance between red and green under low illumination and establish BP neural network model illuminate differences change with illumination and distance. Integrate recognition results from red and green obstacle and use Taylor's formula to establish binary quadratic generalized regression model and neural network model of cognition result change with the speed and distance. Utilize distance recognition results under the same conditions from day and night establish difference between day and night change with velocity and distance in BP network model.5.Record and study eye movement parameters in course of distance cognition by eye tracking system. Analyze laws of fixation parameters variance and study the influence of velocity and space distance on dynamic visual feature in course of experiment.