【摘要】：隨著道路運(yùn)輸事業(yè)的快速發(fā)展,各國機(jī)動(dòng)車保有量迅速增加,道路交通事故數(shù)量與日俱增。然而90%的道路交通事故與駕駛員因素有關(guān),其中駕駛分心往往是造成交通事故的主要誘因。近年來,打車軟件的使用人數(shù)飛速增長,駕駛中操作打車軟件的分心現(xiàn)象也愈來愈普遍,由此誘發(fā)的駕駛安全問題也備受關(guān)注。因此,本文對(duì)駕駛中操作手機(jī)打車軟件誘發(fā)的駕駛分心行為進(jìn)行研究,建立駕駛分心檢測模型,對(duì)改善道路交通安全狀況,降低交通事故發(fā)生頻率具有重大意義。本文首先介紹了駕駛安全與駕駛分心行為的關(guān)系,闡明駕駛分心行為是引發(fā)交通事故,導(dǎo)致駕駛安全問題的重要原因。系統(tǒng)梳理了手機(jī)打車軟件使用特點(diǎn),以《中華人民共和國道路交通安全法實(shí)施條例》等法律為依據(jù),結(jié)合本校駕駛模擬器的實(shí)際情況,篩選出本文研究的四類分心操作:藍(lán)牙通話、與乘客交談、查看信息、信息輸入,手機(jī)支架的擺放位置為儀表臺(tái)。隨后,在西南交通大學(xué)自主研發(fā)的大型駕駛模擬器上完成了基于手機(jī)打車軟件使用的駕駛模擬實(shí)驗(yàn)。對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行分析,結(jié)果表明:常規(guī)場景中,有油門開度、縱向速度、縱向加速度、橫向加速度、方向盤轉(zhuǎn)角、方向盤轉(zhuǎn)角角速度六項(xiàng)數(shù)據(jù)標(biāo)準(zhǔn)差在正常駕駛與四類分心駕駛情況下表現(xiàn)出顯著差異,同時(shí)駕駛員個(gè)體差異對(duì)六項(xiàng)指標(biāo)數(shù)據(jù)結(jié)果沒有顯著影響;非常規(guī)場景中,駕駛員在進(jìn)行駕駛分心操作時(shí),其對(duì)危險(xiǎn)刺激的反應(yīng)時(shí)顯著增長,同時(shí)駕駛員個(gè)體差異也會(huì)對(duì)反應(yīng)時(shí)的結(jié)果造成顯著影響。駕駛員反應(yīng)時(shí)的變化會(huì)直接導(dǎo)致避撞方式的改變,但是通過改變避撞方式的補(bǔ)償行為并未降低交通事故率,駕駛員進(jìn)行駕駛分心操作時(shí)的事故率都顯著上升。最后,將駕駛狀態(tài)分成了三類,即正常駕駛狀態(tài)、認(rèn)知分心駕駛狀態(tài)、視覺分心駕駛狀態(tài);基于車輛橫縱向控制指標(biāo)差異性分析結(jié)果,構(gòu)造了駕駛分心檢測參數(shù)集;運(yùn)用支持向量機(jī)理論搭建了駕駛分心檢測模型。隨后,隨機(jī)選取訓(xùn)練樣本集和測試樣本集對(duì)模型進(jìn)行訓(xùn)練與驗(yàn)證,研究結(jié)果顯示:該模型對(duì)正常駕駛狀態(tài)、認(rèn)知分心駕駛狀態(tài)、視覺分心駕駛狀態(tài)的平均正確檢測率較高,分別為82.5%、80%、92.5%;將視覺分心駕駛狀態(tài)與認(rèn)知分心駕駛狀態(tài)誤判為正常駕駛狀態(tài)的概率都低于8%;模型總體檢測效果較好,可用于駕駛分心檢測。
[Abstract]:With the rapid development of road transportation, the number of motor vehicles in various countries is increasing rapidly, and the number of road traffic accidents is increasing day by day. However, 90% of road traffic accidents are related to driver factors, in which driving distraction is often the main cause of traffic accidents. In recent years, the number of people using ride-hailing software has increased rapidly, and the distractions of driving software are becoming more and more common, and the driving safety problems caused by this phenomenon have also attracted much attention. Therefore, this paper studies the driving distraction behavior induced by mobile phone hailing software in driving, and establishes the driving distraction detection model, which is of great significance to improve the road traffic safety and reduce the frequency of traffic accidents. This paper first introduces the relationship between driving safety and driving distraction behavior, and expounds that driving distraction behavior is an important cause of traffic accidents and driving safety problems. This paper systematically combs the characteristics of the use of mobile phone ride-hailing software, based on the regulations of the people's Republic of China on the implementation of the Road Traffic Safety Law and other laws, combined with the actual situation of the driving simulator in our school. Four kinds of distractions studied in this paper are screened out: Bluetooth conversation, talking to passengers, viewing information, information input, and the location of mobile phone bracket is the dashboard. Then, the driving simulation experiment based on mobile phone ride hailing software is completed on the large driving simulator developed by Southwest Jiaotong University. The experimental data are analyzed. The results show that there are throttle opening, longitudinal velocity, longitudinal acceleration, transverse acceleration and steering wheel angle in the conventional scene. The standard deviation of the six data of steering wheel angular speed showed significant difference between normal driving and four types of distracted driving, and the individual difference of driver had no significant effect on the results of the six indexes. In the unconventional scene, when the driver is distracted, his response to dangerous stimuli increases significantly, and the individual difference of the driver also has a significant impact on the response time results. The change of driver's reaction time will directly lead to the change of collision avoidance mode, but the compensation behavior of changing collision avoidance mode does not reduce the traffic accident rate, and the accident rate of driver when driving distraction operation is significantly increased. Finally, the driving state is divided into three categories, namely, normal driving state, cognitive distracted driving state and visual distracted driving state. Based on the difference analysis results of vehicle horizontal and longitudinal control indexes, the parameter set of driving distraction detection is constructed. Based on the theory of support vector machine (SVM), a driving distraction detection model is built. Then, the training sample set and the test sample set are randomly selected to train and verify the model. The results show that the average correct detection rate of the model for normal driving state, cognitive distracted driving state and visual distracted driving state is high. It was 82.5%, 80% and 92.5% respectively. The probability of misjudging both visual distracted driving state and cognitive distracted driving state to normal driving state is less than 8%. The overall detection effect of the model is good and can be used for driving distraction detection.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.254

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