Sensitivity Study of Highway Tunnel Light Environment Parameters Based on Pupil Change Experiments and CNN Judging Method

Abstract

There is a sparsity of research regarding the nonlinear relationship between the sensitivity of the light environment parameters in the middle section of the tunnel under multi-factor conditions in multiple samples. Due to the lack of research, the present study was conducted in order to investigate said relationship. To determine the parameters of the eye-movement characteristics required for the convolutional neural network prediction evaluation, a tunnel simulation model was established using DIALux10 simulation software and a series of dynamic driving tests were conducted based on an indoor simulation experimental platform. Further, through employing the residual network ResNet to extract data features and the pyramidal pooling network module, a convolutional neural network judging model with adaptive learning capabilities was established for investigating the nonlinear relationship of sensitivity of light environment parameters. Following the test, the degree of influence on the diameter of the pupil for the different levels of each factor were: the optimal configuration of the staggered layout on either side of the lamp arrangement, the optimal 3 m height under the different sidewall painting layout height conditions, the optimal green painting color under the different sidewall painting color conditions, and the optimal 6500 k under different LED light source color temperature conditions. The results of the present study serve to expand the use of the convolutional neural network model in tunnel light environment research and provide a new path for evaluating the quality of tunnel light environment.