## 1 Introduction  
The safety and comfort of driving, along with the vision of those driving, are all directly impacted by the tunnel lighting environment. Due to the adverse effects on drivers' pupil constriction and visual fatigue, the tunnel luminaires, light source, power, layout pattern, and equipment spacing of luminaires are typically the primary focuses in research on tunnel lighting environments [1, 2, 3]. LED light sources are common luminaires in tunnel light environments. The color temperature and spectral distribution of LED light sources directly correlate to the safety of the tunnel LED lighting environment and visual recognition distance [4, 5, 6]. Recognition needs vary under different color temperature conditions, and reducing both luminance and color temperature is more effective than reducing only the luminance when evaluating daytime tunnel cavity section lighting [7, 8]. From the results of studies on visual efficacy, it has been demonstrated that changes in visual efficacy delay or shorten driver emergency response time [9, 10]. Following the analysis of prior research on tunnel light environments, it was observed that the current research mainly revolves around the color temperature and luminance of light sources as well as visual efficacy. Traditional research of tunnel light environments has usually been quantitative with the focus being the indicators of one-to-one correspondence between quantity and quantity. However, tunnel light environments are complex systems in which the relationship between factors is mutual and nonlinear, so it is necessary to adopt a nonlinear research method when studying the tunnel light environment system. In the study of tunnel light environments, researchers often obtain a series of data relating to tunnel light environment through field tests or simulation experiments. In regard to driver visual adaptation characteristics, researchers proposed a method to determine the brightness of the tunnel entrance section [11; 12; 13], and established both a visual adaptation model [14; 15] and a tunnel lighting brightness adaptation model. Additionally, concerning the comfort and safety characteristics of the light source, the color temperature regulation index evaluation model [16; 17; 18] and the color temperature and heart rate reaction analysis model [19; 20] were established. The color temperature of the light source has also been linked to the pupil area size, operation reaction time, and heart rate of the driver. Further, DIALux software has often been employed in experiments on the characteristics of the different sections of tunnels. Researchers have also either simulated and constructed experimental scenarios of tunnel luminaire lighting to calculate and optimize tunnel designs [21; 22; 23], or simulated and modeled specific sections to perform tunnel light environment simulation tests [24; 25; 26]. After reviewing the literature on the analysis methods of tunnel light environment data, it was observed that the previous light environment data, due to its research objectives and the single nature of the research content, led to the data analysis and collation techniques being primarily based on the analysis of mathematical and theoretical models, with the analysis method of discriminating both data and scene images not being utilized. Due to the development of sensor acquisition, information storage, and analysis technologies, analysis techniques based on digital image processing are being increasingly applied to the subject field of tunneling. In tunnel envelope studies, the implementation of artificial neural network image processing is extensively employed for predicting and analyzing the extrusion deformation of tunnel envelope. Said process involves combining neural network models [27; 28] and utilizing the backpropagation (BP) neural network and its optimized morphology for tunnel envelope mechanics [29; 30; 31].