为进一步分析驾驶员在毗邻城市地下道路出入口分合流段的视觉特性及风险情况，基于实车试验所采集的数据，通过构建线性拟合模型及计算瞳孔面积增长率分析了瞳孔面积变化规律，并基于K-medoids聚类法对驾驶员的注视兴趣区域进行划分，分析了驾驶员注视行为特征。通过构建博弈论-可拓云评价模型针对毗邻城市地下道路出入口分合流路段的行车风险进行了评价。结果表明，驾驶员瞳孔面积在入口段呈线性增长而在出口段呈现线性减小规律，整体变化毗邻入口合流段>毗邻入口分流段>毗邻出口分流段>毗邻出口合流段，同时驾驶员在毗邻城市地下道路出入口分合流段受多方面因素的作用存在一定行车风险，针对评价结果与实际调研情况相结合，分析了各路段存在风险诱因，并在此基础上提出了优化改善方案。

In order to further analyze the visual characteristics and risk conditions of drivers at the confluence sections of entrance ramps at adjacent urban underground roadways, data collected from real-vehicle experiments were used. A linear fitting model was constructed and the pupil area growth rate was calculated to investigate the patterns of pupil area changes. The K-medoids clustering method was employed to classify the regions of interest in drivers' gaze patterns, and the characteristics of drivers' gaze behavior were analyzed. A game theory-extension cloud evaluation model was constructed to evaluate the driving risks at the confluence sections of entrance ramps at adjacent urban underground roadways. The results show that the pupil area increases linearly in the entrance section and decreases linearly in the exit section. The overall load follows the order of adjacent entrance confluence section > adjacent entrance split section > adjacent exit split section > adjacent exit confluence section. Additionally, drivers face certain driving risks due to various factors at the confluence sections of entrance ramps at adjacent urban underground roadways. Based on the evaluation and actual survey, the risk factors for each section were analyzed, and optimization and improvement suggestions were proposed.