PDF(Pubmed)
Abstract：
Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.
摘要：
冬季恶劣天气下路面状况的准确检测对交通安全至关重要。为了促进安全驾驶和有效的道路管理，这项研究提出了一个准确和可推广的数据驱动的学习模型，用于估计路面状况。机器模型是支持向量机(SVM)，已成功应用于各个领域，和核函数(线性，高斯,还采用了具有软边缘分类技术的二阶多项式）。两种学习者设计(一对一，一对一)将其应用扩展到多类别分类。除了这个非概率分类器，这项研究通过将sigmoid函数应用于经训练的SVM获得的分类分数来计算属于每个组的后验概率。结果表明,所有分类器的分类误差,不包括一元对全线性学习器，低于3%，从而准确地对路面状况进行分类，并且所有一对一学习者的泛化性能都在4％的错误率之内。结果还表明，后验概率可以分析与危险路面条件的高概率相对应的某些大气和路面条件。因此,这项研究证明了数据驱动的学习模型在准确分类路面条件方面的潜力。
