中文摘要：

低等级公路环境下的行车速度变化特征比较复杂，已有的基于几何线形指标的车速模型不足以全面地表述这种特征．通过大量实车实验发现，低等级公路环境下驾驶人所采用的行车速度和其视觉感知到道路条件之间存在很好的相关性．驾驶人感知的道路条件分为视觉车道信息和视觉路侧环境信息．采用Catmull-Rom样条曲线拟合的视觉车道模型能够反应驾驶人感知的低等级公路几何特征，根据视觉车道模型形状参数，使用遗传算法优化BP神经网络（GABP）建立了基于几何信息感知的几何车速模型．同时，基于路侧环境信息利用Logistic回归模型建立了路侧车速修正模型．将上述模型结合起来，形成了基于驾驶人视觉感知的低等级公路行车车速预测方法．由此方法计算的驾驶人行车速度与实测情况吻合性好，能够很好地描述驾驶人在低等级公路环境下通过对道路条件视觉认知而产生行车速度的行为特征，是低等级公路运行车速预测计算的一种有效方法，不仅可以作为道路安全评价的基础，也可以为基于行车速度的公路几何设计提供有力支持．

英文摘要：

Variation characteristics of the driving speed are relatively complicated on low-grade highways, so that the current speed calculation models based only on geometric alignment indexes cannot depict these features comprehensively. According to numerous real vehicle experiments, it can be found that there is a good correlation between the driving speed and road conditions perceived bydrivers＇ vision. These road conditions are divided into the drivers ＇ visual lane information and visual roadside information. The drivers＇ visual lane model fitted by the Catmull-Rom spline could represent the geometric characteristics of low-grade highways obtained from drivers＇ perception. The back-propagation neural network optimized by the genetic algorithm （GABP） was used to establish the geometric speed model based on the shape parameters of the drivers＇ visual lane model. Meanwhile, the roadside driving speed correction model was presented by the logistic regression model on the basis of roadside information. These models were combined to form a low-grade highway driving speed prediction method from drivers＇ visual perception. The driving speed computed with this method agrees well with the measured data, which can well describe the behavioral characteristics that drivers determine driving speeds through their visual perception in the low-grade highway environment. It is an effective method for the driving speed forecast calculation on low-grade highways, which can not only serve as a basis for the evaluation of road safety, but also provide a strong support for the highway geometric design based on the driving speed.