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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/2274


    Title: 應用駕駛績效預測車輛碰撞風險之研究;Prediction of Collision Risks by Application of Driving Performance
    Authors: 張順惠;Shun-Hui Chang
    Contributors: 機械工程研究所
    Keywords: 感知時間;警示系統;駕駛績效;事故風險;perception time;warning system;driving performance;crash risk
    Date: 2009-06-22
    Issue Date: 2009-09-21 11:42:32 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 駕駛人的用路安全愈來愈受到重視,但交通事故導致死亡與受傷的件數卻有逐年上升的趨勢。常見將交通事故歸因為人、車、路因素中,當這三者之間發生衝突時,駕駛人採取規避動作所導致的結果,即有可能造成(或沒有造成)事故的發生。駕駛行為的優劣表現牽連到交通事故的迴避能力,本研究將探討駕駛人在駕駛任務上的表現,量測駕駛績效以評估碰撞風險。本研究使用駕駛模擬器建置虛擬場景,以應用研究方式探討碰撞風險的評估方法、影響因素、貢獻程度,透過碰撞風險的預測模型,提供改善交通安全之參考依據。 以50名實驗受測者進行建立駕駛績效綜合指標之應用研究,實驗設計為2×3,比較受測者性別(男、女)及語音警示內容(無、嗶嗶聲、說話聲)對駕駛績效之影響,實驗結果顯示性別對車道偏差量平均值及標準差有明顯的影響,性別及警示系統對車道偏差量標準差具有交互作用。使用主成份分析方法建立駕駛績效綜合指標,研究結果顯示主成份分數愈高表駕駛人的危險性愈大,當感知時間愈長、車速平均值愈高、車速標準差愈小、車道偏差量平均值愈大、車道偏差量標準差愈大,相對的駕駛操控風險愈高。 以30名受測者進行駕駛績效與事故風險因果關係之應用研究,探討年輕駕駛人行經路口,交通因子(路口防撞警示系統、事件路口位置、每星期開車天數)對駕駛績效與碰撞之影響。本研究以路徑分析探討駕駛績效與事故風險之因果關係,實驗結果顯示警示系統對碰撞風險沒有直接的影響,是透過改善駕駛績效來間接影響到碰撞發生機率。此外,事件路口的位置關係到駕駛事故經驗,駕駛人的事故經驗可降低後續的碰撞風險,其對駕駛績效及碰撞皆有直接影響,亦會透過駕駛績效降低碰撞風險。關於每星期開車天數的駕駛經驗,對駕駛績效沒有直接影響,但對碰撞有直接影響。 以28名受測者進行駕駛績效對碰撞風險預測之應用研究,實驗為2×3的實驗設計,比較交通肇事地點(直線路段、交叉路口)及語音警示內容(無、嗶嗶聲、說話聲)對駕駛績效與碰撞之影響。實驗結果顯示不同的交通肇事地點有不同的資訊來源需求,因此,在不同的交通肇事地點有不同的駕駛績效。良好的警示聲音可讓人有適當的動作以避免碰撞。使用邏輯斯迴歸分析進行碰撞風險之預測,得知感知反應時間對碰撞的影響最大。 研究結果顯示使用主成份分析建立綜合指標、以路徑分析決定因果關係、以邏輯斯迴歸分析預測碰撞,皆有助於瞭解駕駛績效與碰撞風險之關係。後續可擴充應用於其他主題研究,以提供改善交通安全之建議。 Driving safety has been accorded increased attention in recent years. However, the casualty caused by traffic accidents has continuously been increasing over the years. Traffic accidents can be attributed to reasons related to “driver, vehicle, and the road.” In the case of conflict between these three factors, the driver’s avoidance action may (or may not) cause accidents. Driving performance also relates to the ability to avoid traffic accidents. Against this backdrop, this study will explore and measure driving performance in order to assess the crash risks. We developed a virtual scenario through a driving simulator, explored the assessment method of crash risks, examined the influential factors and degree of attribution by applying the study method, and provided the reference basis for the improvement of traffic safety through the prediction model of crash risks. This study established comprehensive indexes of driving performance based on 50 subjects who recruited the test and compared the influence of gender (male, female) and auditory alarm contents (null, beep sound, speech message) on driving performance. The experimental result indicates the interactive influence between gender and the alarm system on the standard deviation of lane deviation. We developed the comprehensive indexes through principal component analysis method. The study results indicate that the risk of the driver is bigger when the fraction of the principal component is higher, and the risk of operation is higher when the time of perception is longer, the average speed is higher, the standard deviation of speed is smaller, the average lane deviation is higher, and the standard offset of lane deviation is bigger. This study investigated the causality between driving performance and accident risks through 30 subjects who recruited the test in order to explore the influence of intersections of young drivers and traffic factors (intersection collision warning system, location of the accident intersection, days of driving per week) on driving performance and crash. The study employed path analysis to explore the causality between driving performance and accident risks. The result indicates that the alarm system has no direct influence on crash risks, and the probability of crash can be reduced by improving driving performance. Moreover, the location of intersection accident is related to experiences of driving accidents. These accident experiences can reduce the risks of subsequent crash, and they have a direct influence on both driving performance and crash. Thus, risks of crash can be reduced by improving driving performance. Moreover, driving experience, that is, days of driving per day, has a direct influence on crash but not on driving performance. This study investigated studied the predication of crash risks by examining the driving performance of 28 subjects who recruited the test. We also compared the influence of the location of traffic accidents (roadway segment, intersection) and auditory alarm contents (null, beep sound, speech message) on driving performance and crash. The result indicates that different locations of traffic accidents have different demands for information source. Proper acoustic warning allows driver to avoid crash through proper actions. We conducted logistic regression analysis on the prediction of crash risks and found that the perception-reaction time has the largest influence on crash. The findings indicate that building comprehensive indexes with principal component analysis, deciding the causality with path analysis, and predicting crash by logistic regression analysis will help us understand the relationship between driving performance and crash risks. This can be expanded to other topics for study in the future in order to improve traffic safety further.
    Appears in Collections:[機械工程研究所] 博碩士論文

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