本文選題：風(fēng)險感知效用 + 視覺行為及特性�。� 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：國內(nèi)外研究表明,在大多數(shù)交通事故中,人的因素是最主要的致因,駕駛?cè)诵熊囘^程中對道路交通風(fēng)險感知的有效與否是交通安全的重要環(huán)節(jié)。道路交通系統(tǒng)中駕駛?cè)?0%的信息是通過視覺系統(tǒng)執(zhí)行,其風(fēng)險感知尤其重要,因此研究駕駛?cè)艘曈X行為及特性對于道路交通人因安全研究具有必要性。本課題以不同風(fēng)險感知能力的駕駛?cè)艘曈X行為及特性為切入點,基于昆明理工大學(xué)駕駛模擬系統(tǒng)平臺,應(yīng)用iViewXTMHED4頭戴眼動儀及BeGaze3.5駕駛?cè)搜蹌訑?shù)據(jù)分析軟件,研究不同風(fēng)險感知效用駕駛?cè)艘曈X行為及特性的差異性及其與風(fēng)險感知效用的關(guān)系,建立駕駛?cè)艘曈X行為及特性與風(fēng)險感知效用關(guān)系的模型。具體內(nèi)容如下:首先,招募31名符合試驗要求的駕駛?cè)俗鳛楸敬卧囼灥谋辉囌?在五個模擬風(fēng)險駕駛情境下進(jìn)行風(fēng)險感知實驗,實時記錄其模擬駕駛過程中的駕駛行為數(shù)據(jù)、眼動數(shù)據(jù)及車輛運(yùn)行數(shù)據(jù),并基于模擬風(fēng)險駕駛情境完成主觀風(fēng)險度評價主觀問卷測評;應(yīng)用駕駛?cè)孙L(fēng)險感知效用理論,對被試駕駛?cè)说娘L(fēng)險感知效用進(jìn)行計算,采用聚類分析方法將31名被試分為保守型、中間型、激進(jìn)型和復(fù)合型等四種風(fēng)險感知類型。其次,對不同風(fēng)險感知效用類型駕駛?cè)说囊曈X行為及特性進(jìn)行研究。通過BeGaze3.5眼動數(shù)據(jù)分析軟件提取被試者注視、掃視、眨眼、眼動等32項數(shù)據(jù);研究確定選擇其中8項指標(biāo)作為本次實驗的實驗指標(biāo),即被試駕駛?cè)说淖⒁暣螖?shù)與時間;掃視速度、幅度與時間..眨眼時間、頻率及次數(shù)。視覺特性研究發(fā)現(xiàn),被試駕駛?cè)说?項實驗指標(biāo)中,平均注視次數(shù)、每次注視時間、掃視幅度、眨眼頻率、平均眨眼次數(shù)五項指標(biāo)差異性顯著。結(jié)果表明,不同風(fēng)險感知效用類型駕駛?cè)说囊曈X行為及特性有明顯差異。最后,基于計劃行為理論構(gòu)建駕駛?cè)艘曈X行為及特性與風(fēng)險感知效用之間的關(guān)系模型。在對數(shù)據(jù)進(jìn)行量綱一化和擴(kuò)增樣本容量的基礎(chǔ)上,應(yīng)用計劃行為理論構(gòu)建了駕駛?cè)笋{駛態(tài)度、主觀規(guī)范、知覺行為控制、注視特性、眨眼特性、掃視特性等12個變量的風(fēng)險感知效用類型模型框架:選擇結(jié)構(gòu)方程模型構(gòu)建駕駛?cè)搜蹌有袨榕c駕駛?cè)孙L(fēng)險感知效用類型的關(guān)系模型,模型分析表明擬合精度良好,可用于解釋駕駛?cè)搜蹌有袨�、行為意向及風(fēng)險感知效用類型之間關(guān)系,且視覺特性及行為對駕駛行為意向呈現(xiàn)正效應(yīng),而行為意向又對風(fēng)險感知效用類型呈現(xiàn)正效應(yīng)。研究結(jié)果可為駕駛?cè)艘虬踩u估提供新的思路。
[Abstract]:Studies at home and abroad show that in most traffic accidents, the human factor is the most important cause, and the effective perception of road traffic risk is an important part of traffic safety.In the road traffic system, 80% of the information of the driver is executed through the visual system, and its risk perception is particularly important. Therefore, it is necessary to study the visual behavior and characteristics of the driver for the study of human safety in road traffic.Based on the driving simulation system platform of Kunming University of Science and Technology, using iViewXTMHED4 headset eye movement instrument and BeGaze3.5 driver eye movement data analysis software, this paper takes the visual behavior and characteristics of drivers with different risk perception abilities as the breakthrough point, and based on the driving simulation system platform of Kunming University of Technology.This paper studies the differences of visual behaviors and characteristics of drivers with different risk perception utility and their relationship with risk perceived utility, and establishes a model of the relationship between drivers' visual behavior and risk perceived utility.The specific contents are as follows: firstly, 31 drivers who meet the requirements of the experiment were recruited as the subjects of this experiment. The risk-aware experiments were conducted in five simulated risk driving situations, and the driving behavior data of the simulated driving process were recorded in real time.Eye movement data and vehicle operation data, and based on the simulation of risk driving situation to complete the subjective risk evaluation of subjective risk assessment subjective questionnaire, using the driver risk perception utility theory, the test driver risk perception utility calculation.Using cluster analysis, 31 subjects were divided into four risk perception types: conservative type, intermediate type, radical type and compound type.Secondly, the visual behavior and characteristics of drivers with different risk perception utility types are studied.32 items of data such as gaze, scan, blink and eye movement were extracted by BeGaze3.5 software, and 8 of them were selected as the experimental indexes, namely, the number and time of gaze, the speed of scan, and so on.Range and time.Blink time, frequency and frequency.The study of visual characteristics showed that there were significant differences in the average fixation times, fixation time, scan amplitude, blink frequency and average blink frequency among the 8 experimental indexes of the test drivers.The results show that there are significant differences in visual behavior and characteristics among different risk perception utility types.Finally, based on the theory of planning behavior, the relationship between driver's visual behavior, characteristics and risk perception utility is established.Based on the dimensionalization of the data and the expansion of the sample size, the driving attitude, subjective norms, perceptual behavior control, gaze characteristics, blink characteristics of the driver are constructed by using the theory of planned behavior.The risk perception utility type model framework of 12 variables, such as scan characteristics, is selected to construct the relationship model between the driver's eye movement behavior and the driver's risk perception utility type. The model analysis shows that the fitting accuracy is good.It can be used to explain the relationship among driver's eye movement behavior intention and risk perception utility type and the visual characteristics and behavior have positive effect on driving behavior intention and behavioral intention has positive effect on risk perception utility type.The results of the study can provide a new idea for driver's safety assessment.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.25

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 秦雅琴;張紅強(qiáng);熊堅;江良華;;風(fēng)險駕駛模擬情境下駕駛?cè)孙L(fēng)險感知研究[J];交通運(yùn)輸系統(tǒng)工程與信息;2015年02期

2 閆巖;;計劃行為理論的產(chǎn)生、發(fā)展和評述[J];國際新聞界;2014年07期

3 唐智慧;蘭婷;程杰;朱翠翠;;基于FSDT的駕駛員危險感知能力檢測研究[J];安全與環(huán)境工程;2013年04期

4 謝志剛;周晶;;重新認(rèn)識風(fēng)險這個概念[J];保險研究;2013年02期

5 常若松;馬錦飛;陳曉晨;劉新洋;梁超;董悅;;駕駛員危險知覺情境問卷的編制[J];中國健康心理學(xué)雜志;2013年02期

6 伍麟;張璇;;風(fēng)險感知研究中的心理測量范式[J];南京師大學(xué)報(社會科學(xué)版);2012年02期

7 馬健;孫秀霞;;基于效用曲線改進(jìn)的前景理論價值函數(shù)[J];信息與控制;2011年04期

8 張欽昊;;基于眼肌肌電信號分析的眨眼識別[J];計算機(jī)應(yīng)用與軟件;2011年06期

9 莊明科;白海峰;謝曉非;;駕駛?cè)藛T風(fēng)險駕駛行為分析及相關(guān)因素研究[J];北京大學(xué)學(xué)報(自然科學(xué)版);2008年03期

10 段文婷;江光榮;;計劃行為理論述評[J];心理科學(xué)進(jìn)展;2008年02期

相關(guān)博士學(xué)位論文 前1條

1 王延斌;新駕駛員險情感知訓(xùn)練研究[D];清華大學(xué);2010年

相關(guān)碩士學(xué)位論文 前7條

1 劉振興;駕駛?cè)孙L(fēng)險感知特性與駕駛行為關(guān)系研究[D];昆明理工大學(xué);2016年

2 肖將;駕駛?cè)孙L(fēng)險感知效用研究[D];昆明理工大學(xué);2015年

3 王圓圓;風(fēng)險駕駛情境下道路廣告對駕駛?cè)俗⒁饬Φ挠绊慬D];昆明理工大學(xué);2015年

4 張紅強(qiáng);基于駕駛模擬器的情境風(fēng)險度評價[D];昆明理工大學(xué);2014年

5 鄭東鵬;駕駛?cè)宋ｋU感知及影響因素研究[D];上海交通大學(xué);2013年

6 曹黎明;公共風(fēng)險的經(jīng)濟(jì)分析：起因、分類及對策[D];江西財經(jīng)大學(xué);2009年

7 戎靖;交通安全意識與安全行為之間的關(guān)系研究[D];北京交通大學(xué);2008年

，

本文編號：1731679