基于Android与OBD的车载终端及其在驾驶行为评估中的应用研究

发布时间：2019-07-09 08:43

【摘要】：我国是全球最大的汽车生产国和消费国,车联网市场巨大。从汽车这一新兴移动终端到由汽车组成的车联网系统,牵动着我国又一条至关重要的经济脉络。据某研究院数据库显示:截至2016年底,全国机动车保有量达29000万辆,其中汽车19400万辆;机动车驾驶人36000万人,其中汽车驾驶人超过31000万人。在此背景下,汽车保险行业在获得无限发展空间的同时,却由于恶性竞争、交通事故率不断上升等原因,导致车险公司利润率不断下降。因此,根据不同的驾驶行为、车辆历史故障情况等数据设计出差异化的保险方案意义重大。这样就需要借助车载信息系统与OBD(On Board Diagnostic,车辆诊断系统)等技术来采集运行信息并结合大数据挖掘等算法分析驾驶人的驾驶行为习惯及车辆状况。基于上述背景,本文研究开发了一款Android操作系统设备与WEB平台相结合的车载终端,该终端能够实时监控车辆的行驶数据信息与故障状况,把数据实时地上传到服务器,并通过大量车辆数据对驾驶行为评估进行分析与建模。首先,构建了系统架构。将车载终端分成车辆数据采集与故障诊断端、Android客户端和WEB平台三个部分,并对前两个部分的硬件接口进行分析与设计。车辆数据采集与故障诊断端通过OBD接口从车辆内部的ECU实时采集车辆数据,经蓝牙接口将数据传输给Android客户端,Android端使用Cortex-A9处理器,通过无线通信技术把数据上传到WEB平台。其次,对车载终端系统Android客户端和WEB平台的核心功能软件进行开发与设计。Android客户端主要实现Android端与车辆OBD系统的通信、车辆OBD数据的实时采集与上传和地图服务三个核心功能。WEB平台主要实现上传数据的存储与分析、数据图表呈现和数据记录查询三个核心功能。再次,利用逐步回归分析方法对数据进行处理,经显著性检验,建立最优回归数学模型,用以评估驾驶人的行为状况,给车险行业中差异化保险方案提供参考。最后,通过Android客户端功能测试和WEB平台功能测试,验证了本系统可行性和实时性。
文内图片：

图片说明：014-2016年中国汽车保有量及增速Fig.1-1CarownershipandgrowthofChinain2014-2016车载终端平台是驾乘人员获得车联网最终信息的媒介，但目前车载终端应用最多
[Abstract]:China is the largest automobile producer and consumer in the world, and the car network market is huge. From the new mobile terminal to the vehicle networking system composed of cars, another vital economic context of our country is affected. By the end of 2016, there were 290 million motor vehicles, of which 194 million were cars, and 360 million were motor vehicle drivers, including more than 310 million, according to a database of a research institute. In this context, the automobile insurance industry not only obtains unlimited space for development, but also due to vicious competition, traffic accident rate continues to rise and other reasons, resulting in the continuous decline of profit margins of car insurance companies. Therefore, it is of great significance to design a differentiated insurance scheme according to different driving behavior, vehicle historical failure and other data. In this way, it is necessary to collect the operation information with the help of vehicle information system and OBD (On Board Diagnostic, vehicle diagnosis system) and analyze the driving behavior and vehicle condition of the driver with big data mining and other algorithms. Based on the above background, this paper studies and develops a vehicle terminal which combines Android operating system device with WEB platform. The terminal can monitor the driving data and fault status of the vehicle in real time, upload the data to the server in real time, and analyze and model the driving behavior evaluation through a large number of vehicle data. First of all, the system architecture is constructed. The vehicle terminal is divided into three parts: vehicle data acquisition and fault diagnosis, Android client and WEB platform, and the hardware interface of the first two parts is analyzed and designed. The vehicle data acquisition and fault diagnosis terminal collects vehicle data from the ECU inside the vehicle through OBD interface, and transmits the data to the Android client through Bluetooth interface. The Android terminal uses Cortex-A9 processor to upload the data to the WEB platform through wireless communication technology. Secondly, the core function software of vehicle terminal system Android client and WEB platform is developed and designed. Android client mainly realizes three core functions: communication between Android client and vehicle OBD system, real-time collection and upload of vehicle OBD data and map service. Web platform mainly realizes three core functions of uploading data storage and analysis, data chart presentation and data recording query. Thirdly, the stepwise regression analysis method is used to process the data, and the optimal regression mathematical model is established by significance test to evaluate the behavior of the driver and provide a reference for the differentiated insurance scheme in the automobile insurance industry. Finally, the feasibility and real-time performance of the system are verified by Android client function test and WEB platform function test.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.52;TP316

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