基于得分系数的跟车工况驾驶风格识别研究

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

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.

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.