智能车辆在城区交通场景中的多类障碍物识别

针对智能车辆在城区交通场景中的前向障碍物识别,提出了一种基于集成学习改进的二叉树支持向量机(BT-SVM)的多类分类方法。根据城区交通场景中各类障碍物的出现概率、模式类别差异,设计了适用于智能车辆障碍物识别的BT-SVM树型结构;对每个节点分类器,采用AdaBoost集成学习方法进行改进,有效减少了差错积累误差,提高了分类精度和泛化能力。试验表明该方法能有效地对城区交通场景中6类常规障碍物模式进行实时在线识别。

Multi class obstacles recognition for intelligent vehicle in urban traffic scenes

For multi-class obstacles recognition for intelligent vehicle in urban traffic scenes,an improved Binary Tree Support Vector Machine(BT-SVM) based on ensemble learning is presented.Based on the distributing probability and pattern diversity of each obstacle in urban traffic scenes,a compatible tree structure of BT-SVM is designed.An approach based on AdaBoost ensemble learning is applied to reduce the transfer error and improve the accuracy and generalization ability of per-node classifier.The proposed meth...