高速公路匝道区域驾驶信息负荷对交通安全的影响研究

发布时间：2018-04-23 14:59

本文选题：匝道区域 + 交通安全　；参考：《武汉理工大学》2014年硕士论文

【摘要】：我国高速公路交通事故多发，高速公路出口匝道区域事故数在总数中占有很大的比例，提高高速公路出口匝道区域的安全性对于提升高速公路安全水平有重要意义。研究表明，道路高速公路事故90%由人的因素引起，但在与驾驶人因素有关的事故中，41.4%的事故驾驶人无明显违法和主观错误，而是因为驾驶人未能正确认知外部环境。高速公路匝道区域由于环境信息复杂，车速较快，，对驾驶人产生较高的驾驶负荷，易引起驾驶人视认、判断或操作失误，进而影响交通安全。本文利用生理心理测量技术对出口匝道区域信息负荷进行研究，同时分析了不同信息负荷对交通安全的影响。首先，从信息理论的角度对道路指示标志信息进行量化计算，并将其划分为四个等级。在此基础之上，基于E-prime刺激呈现软件及NeuroScan脑电数据采集系统，设计并开展了静态标志信息视认试验，以24名驾驶人为被试，采集驾驶人行为数据和脑电数据。分析结果显示，被试对不同等级信息量标志牌的反应时间有显著差别，且主观负荷与标志信息量等级高度相关，表明标志信息可以作为控制负荷变化的指标；脑电数据结果显示，对于相邻等级标志信息，被试脑波成分所占的比例变化不显著，对于A、C级信息和B、D级信息，被试Beta波和Delta波百分比率变化显著。为了进一步研究高速公路匝道区域信息负荷特性，基于模拟驾驶平台，利用Equivital EQ SEM生理参数采集设备以及脑电采集系统，设计并开展了模拟驾驶场景下高速公路匝道区域信息负荷试验，采集车辆运行数据、生理数据、脑电数据、主观负荷评价数据等，并对试验相关数据进行分析。结果显示，在匝道区域被试对车辆速度控制稳定性减弱；速度对标志视认时间影响显著，模拟驾驶中反应时间明显大于静态试验条件下反应时间，相同信息级别下平均相差1s左右；生理数据显示，标志信息量等级增加，被试心率变化更加剧烈，HRV可以作为负荷的一个敏感指标，但在B、C级信息量下其值无显著差别；脑电数据结果显示，不同信息负荷条件下脑波成分比例未发现显著差异；对脑波进行组合分析，结果显示，不同信息量与组合脑波之间拟合效果较好，表明组合脑波与视认负荷之间存在一定联系。最后，针对模拟驾驶数据中速度差、横向偏移量及视认时间等因素进行分析，将安全水平分为四个等级，分别得到了不同因素与安全水平之间的关系。利用权值因子判断法确定了各参数的权重，并根据计算结果对匝道区安全进行了综合评价分析。
[Abstract]:There are many highway traffic accidents in China, and the number of freeway exit ramp accidents accounts for a large proportion of the total. It is of great significance to improve the safety of expressway exit ramp area in order to improve the safety level of expressway. The research shows that 90% of the accidents on highway are caused by human factors, but 41.4% of the accidents related to drivers have no obvious violation of the law and subjective mistakes, but because the drivers fail to recognize the external environment correctly. Because of the complexity of the environment information and the high speed of the freeway ramp, it has a high driving load on the driver, which is easy to cause the driver to recognize, judge or operate the wrong, and then affect the traffic safety. In this paper, the physical and psychological measurement technology is used to study the information load of exit ramp area, and the influence of different information load on traffic safety is analyzed. Firstly, the information of road indication signs is calculated quantitatively from the point of view of information theory, and it is divided into four grades. On this basis, based on E-prime stimulation presentation software and NeuroScan EEG data acquisition system, a static marking information recognition test was designed and carried out. 24 drivers were selected to collect driver behavior data and EEG data. The results showed that there were significant differences in the reaction time of different grades of information signs, and the subjective load was highly correlated with the level of mark information, which indicated that the mark information could be used as an index to control the change of load. The results of EEG data showed that the proportion of brain wave components was not significantly changed for adjacent grade marker information, but the percentage of Beta wave and Delta wave changed significantly for C level information and D level information. In order to further study the information load characteristics of freeway ramp area, based on the simulated driving platform, Equivital EQ SEM physiological parameter acquisition equipment and EEG acquisition system are used. Design and carry out information load test of freeway ramp area under simulated driving scene, collect vehicle operation data, physiological data, EEG data, subjective load evaluation data, and analyze the relevant data of the test. The results showed that the stability of vehicle speed control was weakened in the ramp area, the speed had a significant effect on the sign recognition time, and the reaction time in simulated driving was significantly higher than that in static test. Physiological data showed that HRV could be used as a sensitive index for the load, but there was no significant difference in the value of HRV under the information level of BHV C, and the physiological data showed that the HRV could be used as a sensitive index for the load with the increase of the level of marker information and the change of HRV in the heart rate of the subjects. The results of EEG data showed that there was no significant difference in the proportion of brain wave components under different information loads, and the combination analysis of brain wave showed that the fitting effect between different amount of information and combined brain wave was better. It is suggested that there is a certain relationship between combined brainwave and visual load. Finally, by analyzing the factors such as velocity difference, lateral deviation and visual recognition time in simulated driving data, the safety level is divided into four grades, and the relationship between different factors and safety level is obtained. The weight of each parameter is determined by using the weight factor judgment method, and the safety of ramp area is comprehensively evaluated and analyzed according to the calculation results.
【学位授予单位】：武汉理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491

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