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題名:大客車防撞警示系統介面設計與駕駛安全評估方法之研究
作者:高桂娟
作者(外文):Kao, kui-Chuan
校院名稱:中華大學
系所名稱:科技管理博士學位學程
指導教授:張靖
陳菀蕙
學位類別:博士
出版日期:2013
主題關鍵詞:駕駛模擬器防撞警示系統心智負荷driving simulatorcollision warning systemmental workload
原始連結:連回原系統網址new window
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大客車因乘載人數多,一但發生事故,傷亡較嚴重,推動大眾運輸之際也需重視大客車行車安全。防撞警示系統能提供緊急狀況訊息給駕駛者,讓駕駛者能即時反應,藉此降低事故發生機率並提高行車安全。過去許多防撞警示系統相關研究已探討防撞警示類型對於小型車輛之影響,然而卻較少進行大客車之研究,此外,亦無比較駕駛者在有無分心情況下對駕駛安全性之影響。因此本研究之主要研究目的即利用駕駛模擬器探討在有無分心任務情形下,防撞警示類型對大客車駕駛安全性的影響;並發展一套駕駛績效統計分析程序作為駕駛安全評估方法,藉此找出是否發生碰撞變數的直接影響、間接影響以及交互影響。模擬場景事件類型包括前方緊急煞車的突發事件和側向汽車切入的側向事件,共有33位受訪者進行模擬實驗。前方事件是否發生碰撞之羅吉斯迴歸模式,其顯著影響因子包括感知反應時間與煞車反應時間,進一步建立感知反應時間ANOVA模式,得知其影響因子為防撞警示類型與有無次要作業之交互影響,此結果與煞車反應時間ANOVA模式結果相似;側向事件是否發生碰撞之羅吉斯迴歸模式,其顯著影響因子為煞車反應時間,另由煞車反應時間ANOVA模式得知影響因子包括感知反應時間、防撞警示類型、有無次要作業以及突發事件類型;此外,本研究以NASA-TLX探討不同警示介面影響駕駛者的心智負荷程度,結果顯示嗶嗶聲、嗶嗶聲+HUD、語音以及嗶嗶聲+語音四種類型的防撞警示系統均較無警示更能減少大客車駕駛者的心智負荷,此與駕駛績效統計分析結果相符。整合前方和側向防撞警示系統,建議以嗶嗶聲+HUD為警示介面設計的優先考量,或採用「嗶嗶聲+語音」為替代方案。
A bus crash will cause serious casualties due to its large capacity. Thus, with promotion of mass transportation, the driving safety of bus is also needed to be taken seriously. The Collision Warning System (CWS) could provide emergency information to the driver for timely response. In this way, it reduces the accident occurrence rate and increases the driving safety. Many previous studies related to the CWS have already explored its influence on the small vehicles, but there’s rare studies concerning bus. Besides, they don’t compare the influence on the driving safety when the driver is with and without distraction. Therefore, the main purpose of this study is to explore the influence of CWS on the driving safety of bus by using the driving simulator when the driver is with and without distraction tasks. It also develops a statistical analysis program of driving performance, which is taken as the driving safety assessment method. By doing so, it is to work out whether there are direct influence, indirect influence and interactive influence on the collision variables. The simulated scenario events include emergency brake of front car and the changing lane of car from side lane, a total of 33 respondents are involved in the simulated experiment. Regarding the logistic regression model for whether front events cause collision, the factors showing significant influence include sensing response time and braking response time. It further constructs ANOVA model for sensing response time. It is found that the influencing factor is the interactive influence between the collision avoidance warning type and whether it has secondary operations, which is similar to the results obtained from ANOVA model for braking response time. Regarding the logistic regression model for whether side events cause collision, the factor showing significant influence is the braking response time. Moreover, as shown by the ANOVA model for braking response time, the influencing factors include sensing response time, collision avoidance warning type, whether it has secondary operation and emergency event type. Besides, this study adopts NASA-TLX to explore the influence degree of different warning interfaces on the mental workload of the driver. The results show that four types of CWS, namely, beep, beep + HUD, voice, and beep + voice, could reduce the mental workload of bus driver than the situation without warning does, which is consistent with the statistical analysis results of driving performance. Integrating the CWSs ahead and on side, it is suggested to take beep+HUD as the prior warning interface design, or take beep+voice as alternative.
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