Automatic Road Environment Classification
Automatic Road Environment Classification
复制标题
DOI：
10.1109/tits.2010.2095499
复制
发表时间：
2011-06-01
影响因子：
8.5
通讯作者：
Breckon, Toby P.
中科院分区：
文献类型：
--
作者：
Tang, Isabelle;Breckon, Toby P.
许多现代车辆中存在的自动驾驶车辆和自适应车辆动力学的不断发展产生了对道路环境分类的需求，即能够从车载车辆传感器确定当前道路或地形环境的性质。在本文中，我们研究了一种低成本相机视觉解决方案的使用，该解决方案能够根据从驾驶员视角相机视图中提取的颜色和纹理特征进行分析，进行城市、乡村或越野分类。从该前向摄像头视图中的多个感兴趣区域中提取基于颜色和纹理分布的特征集，并与训练有素的分类器方法相结合，以解决不同难度的两个道路类型分类问题-{越野，路上}环境确定和{越野，城市，主要/主干道和多车道高速公路/车道}的附加多类道路环境问题。研究了两种说明性分类方法，并根据一系列真实环境数据报告结果。对于{越野、公路}问题，以 1 Hz 的近实时分类率实现了类似 90% 正确分类的最佳性能。
The ongoing development autonomous vehicles and adaptive vehicle dynamics present in many modern vehicles has generated a need for road environment classification-i.e., the ability to determine the nature of the current road or terrain environment from an onboard vehicle sensor. In this paper, we investigate the use of a low-cost camera vision solution capable of urban, rural, or off-road classification based on the analysis of color and texture features extracted from a driver's perspective camera view. A feature set based on color and texture distributions is extracted from multiple regions of interest in this forward-facing camera view and combined with a trained classifier approach to resolve two road-type classification problems of varying difficulty-{off-road, on-road} environment determination and the additional multiclass road environment problem of {off-road, urban, major/trunk road and multilane motorway/carriageway}. Two illustrative classification approaches are investigated, and the results are reported over a series of real environment data. An optimal performance of similar to 90% correct classification is achieved for the {off-road, on-road} problem at a near real-time classification rate of 1 Hz.