基于OBD数据分析的驾驶行为研究

发布时间：2019-03-08 20:21

【摘要】：近年来,随着社会经济发展,国内汽车保有量也在大幅度增加,加强交通安全管理从而有效防止事故的发生成为近年来交通问题研究的重点。驾驶员作为车辆的操作者,其驾驶行为是交通运输安全影响的最主要因素,研究驾驶行为对预防交通事故、促进智能交通及驾驶辅助系统的发展具有重要意义。本文运用车联网相关技术,通过OBD智能车载终端获取车辆行驶数据,采用算法对数据进行挖掘分析,辨识驾驶员的驾驶行为和车辆状态,对异常驾驶行为进行算法识别,依据驾驶行为指标及其他行程数据对驾驶倾向性进行识别和构建驾驶行为评分模型,文章主要内容如下:(1)介绍了驾驶行为数据的获取和简单的预处理过程。对文中采集数据所用的OBD设备车载终端构造、安装位置等做了简单介绍,详细介绍了设备中的数据采集模块,并对相关数据做了预处理。(2)在驾驶行为评价指标研究的基础上,提出了超速、急加速、急减速、急刹车、急转弯、高转速、转速不匹配、长怠速等异常驾驶行为识别原理算法及判断流程,通过实车实验数据分析验证驾驶行为算法识别效果,并对超速行为、急变速行为做详细识别结果分析。(3)对驾驶倾向性相关理论进行阐述,提取驾驶倾向性识别所需要的车辆状态数据和异常驾驶行为数据等特征参数,先利用k-means聚类算法对驾驶倾向性进行分类,再使用BP神经网络对驾驶倾向性样本进行训练及评估,并验证评估效果的可靠性。(4)构建驾驶行为评分模型,使用改进型熵权层次分析法AEW-AHP确定指标权重,相比单一的熵权法和层次分析法计算结果表明,AEW-AHP法更加符合实际。计算得出权重后,再分析指标的备选项和分值,构建驾驶行为评分模型并运用实例进行分析。
[Abstract]:In recent years, with the social and economic development, the number of domestic car ownership is also increasing greatly. Strengthening traffic safety management to effectively prevent the occurrence of accidents has become the focus of research on traffic problems in recent years. As a vehicle operator, the driver's driving behavior is the most important factor affecting traffic safety. It is of great significance to study the driving behavior for preventing traffic accidents, promoting the development of intelligent transportation and driving assistance system. In this paper, we use the related technology of vehicle networking, get the vehicle driving data through OBD intelligent vehicle terminal, use the algorithm to mine and analyze the data, identify the driver's driving behavior and vehicle state, identify the abnormal driving behavior, and carry on the algorithm recognition to the abnormal driving behavior. According to the driving behavior index and other travel data, the driving tendency is recognized and the driving behavior scoring model is constructed. The main contents of this paper are as follows: (1) the acquisition of driving behavior data and the simple preprocessing process are introduced. This paper gives a brief introduction to the structure and installation position of the on-board terminal of the OBD equipment used to collect data, and introduces the data acquisition module of the equipment in detail. (2) on the basis of the research on the evaluation index of driving behavior, the paper puts forward that overspeed, rapid acceleration, rapid deceleration, sharp brake, sharp turn, high speed and speed mismatch. The principle algorithm and judging process of abnormal driving behavior recognition such as long idle speed are analyzed and verified by the analysis of real vehicle experiment data, and the identification effect of driving behavior algorithm is verified for overspeed behavior. The results are analyzed in detail. (3) the related theory of driving tendency is expounded, and the characteristic parameters such as vehicle state data and abnormal driving behavior data are extracted, which are needed for the identification of driving tendency. Firstly, the k-means clustering algorithm is used to classify the driving tendency, then the BP neural network is used to train and evaluate the driving tendency samples, and the reliability of the evaluation effect is verified. (4) the driving behavior scoring model is constructed. The improved entropy weight analytic hierarchy process (AEW-AHP) is used to determine the index weight. Compared with the single entropy weight method and the analytic hierarchy process (AHP), the results show that the AEW-AHP method is more practical. After the weight is calculated, the reserve options and scores of the indexes are analyzed, and the driving behavior scoring model is constructed and analyzed with an example.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491.25

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