为使先进驾驶人辅助系统更具人性化及个性化，提高智能车辆的驾乘安全性和舒适性，提出一种基于纵向激励工况的驾驶习性分类及辨识方法． 以前车车速信号的周期性及突变性为依据，设计 6 种前车典型纵向激励工况，并通过实车道路试验完成 64 位驾驶人的数据采集． 然后，采用客观粒子群聚类和主观量表分析相结合的分类方式，实现典型驾驶习性的分类和习性类型的定义． 比较各工况下的分类结果，确定纵向最优激励工况组为正弦工况 3 和阶跃工况 3． 建立基于多维高斯隐马尔科夫过程的驾驶习性辨识模型，依据辨识准确率得到最优模型输入信号组，利用正交试验法优化模型中的关键参数． 结果表明，基于纵向激励的驾驶习性分类及辨识方法能够得到更好的分类和辨识准确率．

A research on longitudinal stimuli-based classification and recognition for driving style were carried out to make Advanced Driver Assistance System ( ADAS) work in a more human-like or personalized way and to im- prove the safety and comfort for intelligent vehicles． Six typical longitudinal driving stimuli of the leading vehicle were designed based on the periodicity and mutability of the leading vehicle's speed，and data collection for 64 dri- vers was conducted in field test． The corresponding driving style was defined and classified by combining particle swarm optimization clustering ( PSO-Clustering) method with subjective questionnaire． The optimal longitudinal stimulus set，the Sine NO． 3 and Step NO． 3，was obtained by comparing the classification results under different stimulus． The recognition model for driving styles based on the multi-dimension Gaussian hidden Markov process ( MGHMP) was modeled． And the optimal model input set was obtained based on the recognition accuracy and key parameters were optimized by the orthogonal test method． Ｒesults show that the longitudinal stimuli based classifica- tion and recognition for driving styles can achieve better classification and identification accuracy．