人机共驾是智能车发展中必须经历的一个重要阶段，而人机切换时机选择是人机共驾需要解决的一个关键问题。为此，文中以实车实验采集的数据为依据，根据驾驶人经验及经K-均值聚类得出的危险态势等级对驾驶模式选择方式( 安全驾驶、进行警示和自动切换) 进行了标定。通过引入车速均值、加速度标准差、车头时距、前轮转角标准差、车道偏离量以及驾驶人经验等6 项指标作为特征向量，提出了基于径向基核函数序列最小优化算法( SMO) 的智能车驾驶模式选择模型。并以决策树、径向基神经网络、支持向量机( SVM) 作为对照。研究结果表明，文中提出的基于SMO 方法的驾驶模式识别模型的准确率达到91． 7%，相较于其他3 种识别方法具有较大的优越性．

In the development process of intelligent vehicles,it is a necessary and important stage that manual driving and automatic driving jointly play their roles,of which one key problem is selecting an appropriate take-over time from manual driving to automatic driving when a risky situation occurs.In order to improve the driving safety,according to the data collected from a real vehicle test,driving modes are divided into safe driving,warning driving and automatic driving,based on both the driver’s report and the risky situation levels obtained by means of the K-means clustering.Then,by selecting six impact factors ( namely,the average of speed,the time to headway,the standard deviation of steering,the standard deviation of acceleration,the distance away from the lane and the driver's experience) as the feature vectors,a driving mode selection model of intelligent vehicles is constructed based on the sequential minimal optimization ( SMO) algorithm with the radial basis function ( RBF) .Moreover,the constructed model is compared with the algorithms of ID3,RBF network and SVM.The results show that the constructed model achieves an accuracy of up to 91. 7%，which is significantly superior to those of the other three algorithms.