基于得分系数的跟车工况驾驶风格识别研究

采用NGSIM数据库的数据，选用THW和ITTC参数来评价该工况下的碰撞危险等级，提出了可实现快速驾驶风格识别的客观性得分系数SCO的评价方法，以0~1之间的标准数衡量驾驶员在采样时间段内的驾驶激进程度；分析了SCO评价方法决策边界出现误判的情况，基于K-Means聚类算法对SCO的准确性进行了评价.研究结果表明，相比有级分类，SCO评价方法具有更好的容错性和实时性，其结果可达到95.54%的总体准确率或4.46%的边界误判率，将研究所获得的模型参数和评价方法应用于新的数据场景，也可获得94%的总体准确率.基于SCO评价方法可构建实时易用的驾驶风格识别系统，以实现更加符合驾驶要求的个性化协同控制策略. 

Driving Style Recognition Based on the Score Coefficient Under the Following Condition

Based on NGSIM database, THW and ITTC were selected as parameters to evaluate the collision risk level. And a rapid recognition metric, called the score coefficient of objectivity (SCO), was proposed to measure the driving radicalness in the sampling period with a standard value between 0 and 1. Furthermore, the decision boundary of SCO was analyzed to avoid miscarriage of justice. The accuracy of the score coefficient of objectivity classification was evaluated based on K-Means clustering algorithm. The results show that, compared with traditional classification algorithms, the new method can provide a better veracity and real-time performance. The overall accuracy rate can reach up to 95.54% and the boundary miscarriage of justice can reduce to 4.46%. When the model parameters and evaluation methods are applied to new condition, the 94% overall accuracy rate can also be obtained. Based on the new method, a real-time and convenient driving style recognition system can be developed to achieve a cooperating and individuation control for the advanced driving.