基于车辆总线数据时序分析的驾驶行为辨识方法研究

项目名称： 基于车辆总线数据时序分析的驾驶行为辨识方法研究

项目编号： No.51308426

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 建筑科学

项目作者： 张良力

作者单位： 武汉科技大学

项目金额： 25万元

中文摘要： 人的驾驶行为失常是致使道路交通事故发生的主要原因，采用实际车辆构建试验平台并开展机动车驾驶行为分析、辨识、预测是当前道路交通安全领域研究的热点。本研究以实车驾驶试验平台为依托，以时间序列分析方法为理论依据，通过对车辆总线时序数据特性分析、驾驶行为辨识建模、模型参数估值等内容开展理论研究，达到利用车辆自有数据即可辨识出驾驶行为的目的。分析实车驾驶试验数据的系统特性、统计特性、频谱特性和动态特性，是从系统辨识的角度确立驾驶行为自回归滑动平均模型结构的前提，利用递推最小二乘法的模型参数实时估值方法对模型参数进行自适应修正，可满足不同驾驶个体行为辨识的要求。本研究尝试从系统辨识角度对驾驶行为进行剖析和转化利用，综合运用了交通工程学、车辆工程学、数理统计等理论与方法，在学术上可促进信息科学与交通工程的学科交叉。在应用方面可为研发基于车辆总线数据的车载安全驾驶预警系统提供新思路和理论依据。

中文关键词： 驾驶安全；驾驶行为辨识；时间序列分析；分形特征；碰撞风险评估

英文摘要： Disorders of driver's behavior are the main causes to road traffic accidents. To solve those problems, researches on vehicle driver's behavior analysis, identification, and prediction are carried out with the driving experimental platform constituted by real vehicle. That is popular technical solution in current road traffic safety research areas. The applying project is to identify the driver's behavior on basis of data derived from vehicle data-bus. For that purpose, test data for analysis will be collected by real vehicle driving experimental platform. And the main theoretical method is time series analysis according to the features of real vehicle data. Specific content of the project includes characters analysis of data collected with the real vehicle driving experimental platform, identification modeling of driver's behavior, and valuation method for model parameters. Analyzing test data from the driving experimental platform for their system character, statistics, spectrum and dynamic is the premise to build the structure of an auto-regressive and moving-average (ARMA) model for driver's behavior identification. In order to meet the demand of behavior identification made by different drivers, recursive least squares method will be introduced to valuate and correct parameters of the ARMA model online. The

英文关键词： driving safety；driver's behavior identification；time series analysis；fractal characteristics；collision risk estimation