基于驾驶人行为和车辆运行状态变化的驾驶分心识别方法研究

项目名称： 基于驾驶人行为和车辆运行状态变化的驾驶分心识别方法研究

项目编号： No.61473046

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 袁伟

作者单位： 长安大学

项目金额： 80万元

中文摘要： 驾驶分心（如开车时使用车内信息系统或手机）会严重威胁交通安全，国外最新研究表明，近80%的碰撞和65%的临界碰撞都与驾驶分心有关。本项目拟在汽车试验场和实际道路上开展实车驾驶试验，实时采集驾驶人在分心和未分心驾驶过程的眼动数据、操作行为数据与车辆运动状态数据，深入分析这些参数在受到视觉分心、操作分心、认知分心等驾驶分心次任务及其组合影响时的变化特点与差异；通过对测试数据进行显著性检验，并基于多小波理论对测试参数进行变换，剥离与分心因素不相关的其他信息的影响，从而提取驾驶分心特征参数集；最后采用核主成分分析法和人工神经网络技术分析各特征参数对于不同驾驶分心形式及其组合的敏感度，基于稀疏Bayesian理论并运用DHGF四元评价法对各分心识别子模块的识别结果进行综合处理，建立驾驶分心识别模型。所提出的驾驶分心识别方法能为分心预警系统的开发提供理论支撑。

中文关键词： 人机工程学；交通安全；驾驶行为

英文摘要： Driving distraction (such as the use of in-vehicle information systems or mobile phone) will endanger traffic safety seriously. A recent foreign study has shown that nearly 80 percent of all crashes and 65 percent of all near-crashes contain at least one type of driving distraction. In this work, we intend to carry out real car driving tests both in the automotive testing ground and on the road, and acquire drivers' eye movement and operating behavior data, together with vehicle motion state data in the driving processes with and without distraction. The changes in the characteristics and difference of these parameters will be analyzed in-depth when the primary driving task is impacted by secondary driving tasks with visual distraction, operating distraction, cognitive distraction and/or their combinations. By using significant test and the transformation based on the multiwavelet theory of test parameters, the impact of distraction-irrelevant factors and will be peeled, and the driving distraction characteristic parameters set be extracted. Finally by using the kernel principal component analysis (KPCA) method and artificial neural network (ANN) technology, the sensitivity of each characteristic parameter for different driving distraction form and its combinations will be analyzed. Based on sparse Bayesian theory and the four-element evaluation method of DHGF, the identification results of distraction identification sub-module will be analyzed synthetically, and the driving distraction identification model be established. The driving distraction identification method can provide theoretical support for the development of an early-warning system for distraction.

英文关键词： human factors;traffic safety;driving behavior