随着城市隧道从“主观经验设计”向“数据驱动设计”转变，驾驶人对隧道景观的视认感知效果日益成为衡量其安全性与舒适性的重要标准。为揭示城市隧道景观在静态与动态视觉感知条件下的特征差异，并实现对主要景观要素的定量化识别与分类，构建一种融合图像识别与主观感知分析的景观要素提取方法。针对传统城市隧道景观设计多基于静态视角，难以准确反映驾驶人在动态行驶过程中真实视觉体验的问题，通过组织驾驶人实地行车试验，采集动态条件下的视觉感知数据； 同时，利用图像处理技术对数据图像进行特征提取与“斑块”识别，提取隧道景观的静/动态视觉特征。采用Schroeder主次划分法对识别出的景观要素进行分类与评级，以区分主要景观要素与辅助性要素。结果表明： 在动态条件下，驾驶人更关注行车路径两侧及视觉重叠区域中的关键景观要素，合理设置该区域的颜色与图案可显著提升视觉舒适度与安全感；而在静态条件下，驾驶人虽能观察更多细节，但这些细节在实际行车过程中因不易被感知而难以发挥调节视认感知的作用。
As urban tunnels shift from "subjective experience design" to "data-driven design", the visual perception of drivers has become a critical criterion for evaluating safety and comfort. To reveal the differences in urban tunnel landscapes under static and dynamic perception conditions, and to enable quantitative identification and classification of key landscape elements, the authors construct a landscape element extraction method that integrates image recognition and subjective perception analysis. Because conventional urban tunnel landscape designs are mostly based on static perspectives and do not accurately reflect drivers′ real visual experiences during motion, the authors collect dynamic perception data by organizing real driving tests. Image processing technology is used to extract features and identify "blobs" from the data images, thereby distinguishing static/dynamic visual features of the tunnel landscape. The identified landscape elements are then classified and rated using the Schroeder primary-secondary classification method to separate the main landscape elements and the auxiliary ones. The results show that under dynamic driving conditions, drivers focus more on key landscape elements on both sides of the driving path and within overlapping visual fields. Appropriate use of the color and patterns in these areas significantly enhances visual comfort and safety. Under static driving conditions, drivers observe more details; however, these details are difficult to perceive in actual driving and therefore do not contribute to regulating visual recognition perception.
