中文摘要：

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.

英文摘要：

In the development process of intelligent vehicles, it is a necessary and important stage that manual dri- ving 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 dri- ver＇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 con- structed model is compared with the algorithms of ID3, RBF network and SVM. The results show that the construc- ted model achieves an accuracy of up to 91.7%, which is significantly superior to those of the other three algo- rithms.