隧道不同区段的光环境差异会使驾驶人视场环境发生变化，引起驾驶人信息感知错误，威胁行车安全，且视场对比度的动态变化易诱发视觉疲劳与感知能力降低。为揭示隧道动态光环境中视场对比度及其梯度变化对驾驶人视觉功效的影响机制，通过多因素控制试验(目标位置、颜色、亮度、视场对比度耦合控制)，构建“视场对比度动态梯度-视觉功效响应”耦合分析框架，并创新引入RF-SHAP（random forest-Shapley additive explanation）可解释机器学习算法解析多参数非线性作用。结果表明： 1)视场对比度存在拐点效应，入口段视场对比度为0.2~0.4时可缩短反应时间20.5%，视场对比度由0.4提升到0.6时仅缩短反应时间2.3%。2）亮度与视场对比度存在强交互效应，中间段背景亮度提升至8.75 cd/m²时可补偿低视场对比度视觉信号，目标识别成功率达70%(较背景亮度为4.50 cd/m²时提升30%)。3)视野边缘(30°位置)环境敏感性高于中心区(5°位置)，低视场对比度(0.2)与高亮度(6 000 cd/m²)在视野边缘(30°位置)叠加时，反应时间最大增幅达53.1%。4）暖色调目标(红色)较冷色(蓝色)识别效率提升14.2%，尤其在低视场对比度(0.1)时色差影响最显著。5)RF-SHAP模型揭示了视场对比度与亮度为视觉功效主导因子，且存在U形影响曲线，验证了动态光环境参数阈值的工程适用性。

Variations in lighting across tunnel sections alter drivers′ fields of view, causing misperception of information and compromising safety. Additionally, dynamic changes in field contrast induce visual fatigue and reduce perceptual ability. To investigate how field contrast and its gradients influence visual performance under dynamic tunnel lighting, the authors develop a "dynamic gradient-field contrast versus visual performance" analytical framework using a multi-factor controlled experiment, in which target position, color, luminance, and contrast are systematically varied. A random forest-Shapley additive explanation (RF-SHAP) interpretable machine learning algorithm elucidates nonlinear interactions among these parameters. The results indicate the following: (1) A visual field contrast inflection effect exists: in the threshold zone, contrast of 0.2－0.4 reduces reaction time by 20.5%, whereas increasing contrast by 0.2 beyond 0.4 reduces it by only 2.3%. (2) Brightness and contrast show a strong interaction: in the interior zone, increasing background luminance to 8.75 cd/m² compensates for low-contrast signals, raising recognition success to 70% (a 30% improvement over 4.5 cd/m²). (3) Sensitivity at the visual field periphery (30°) exceeds that of the central area (5°); when low contrast (0.2) combines with high luminance (6 000 cd/m²) at 30°, reaction time increases by up to 53.1%. (4) Recognition efficiency for warm-colored targets (red) is 14.2% higher than for cool colors (blue), with chromatic differences exerting the greatest effect under low contrast (0.1). (5) RF-SHAP analysis identifies visual field contrast and luminance as dominant factors, displaying U-shaped influence curves and confirming the practical applicability of dynamic lighting thresholds in engineering design.