A novel approach for modeling driver attention allocation
|關鍵字:||注意力分配;循環;視線移轉;分心;自然駕駛;Attention allocation;Renewal cycle;Vision transition;Distraction;Naturalistic driving|
以注意力分配為題，本研究欲回答下列問題：1) 駕駛人注意力分配應如何呈現？ 2) 駕駛人是否會採取特定的注意力分配型態？ 3) 若有，有哪些型態？ 4) 有哪些變數會影響駕駛人注意力分配？為回答上述問題，本研究首先提出「注意力分配循環」之概念，以前方焦點視為一基準點，將視線移開前方至他處最後再回到前方的循環過程視為注意力分配之基本元件，呈現駕駛視線分配的完整過程。本研究於分析階段採用美國100-car自然駕駛資料庫當中的事件資料庫進行分析，透過序列關聯法則，找出駕駛人將視線在前方與非前方之間移轉的路徑，並透過羅吉特模式之應用，計算駕駛人在不同狀況下，選擇不同類型視線移轉與選擇各焦點的機率分配。
Attention allocation is the key of driving safety, which relies on the adequate distribution of the driver’s attention to the forward area and to other non-forward focal points. However, most representation of attention allocation are the aggregated result of vision transition. It is not able to observe drivers’ microscopic behavior against dynamic changing environment. Moreover, thus far, current methods seem to be over-emphasized on the dominant forward area, causing the observed paths were mostly ones shifting from or heading to the frontal side. The whole process of transiting vision among focal points cannot be observed. Consequently, a mechanism for attention allocation is a critical issue in crash prevention. There are four questions that this study aims to answer: 1) How is driver attention allocation represented? 2) Do patterns of driver attention allocation exist? 3) If yes, what are these patterns? and 4) What are the factors affecting driver attention allocation? To answer these questions, this study proposes the concept of renewal cycle, which is the entire process of drivers glancing at forward side, transiting vision away, and finally transiting vision back to the front. Using the renewal cycle as the basic component of attention allocation analysis, this study is able to represent drivers’ vision transition in a more realistic way. In the section of empirical analysis, this study adopted the event database of 100-car naturalistic driving studies. Sequential rule mining and multinomial logit model were used for generating the patterns and probability of drivers transiting vision among focal points. This study found that over 90% of drivers’ attention allocations were 2-glance renewal cycles, suggesting that drivers usually glance only one off-road focal point, among which the in-vehicle distraction, left mirror and rearview mirror are the three most frequent appeared ones. Among these 2-glance renewal cycles, some were found repeatedly appeared several times, particularly the ones related to in-vehicle distraction and rearview mirror. It suggests a compensation of lost awareness against leading area by separating their long glance off-road into several shorter ones. In addition, drivers prefer not to transit vision from one non-forward focal point directly to another. Instead, they glance at forward side between two non-forward glance for checking the timely status ahead. As for the choices of focal points, four constructs of attributes (Salience, effort, expectancy and value) in SEEV model were included in this model. The result shows that drivers would allocate more attention to the focal point with higher information expectancy and value. On the other hand, less salient and higher effort would inhibit the vision transition. Finally, this study adopted the Perception Reaction Time (PRT) as the reference for setting the maximum time for drivers to transit vision away from the frontal side. It clearly indicated that drivers glancing consecutively at more non-forward focal points in a sequence were more likely to have insufficient time for responding to harmful changes in front of them. In addition to distractions, maneuver intentions, number of glances in a renewal cycle, and their interactions all significantly affected drivers’ attention allocation. As for the current 2.5-s PRT rule, it may not be robust enough to satisfy every situation. Based on the results derived from the 100-car event database, a 3.0-s PRT may be better for designing safer roads. Although the sample drivers adopted in this study were not representative, the preliminary research results were promising and fruitful for potential applications, particularly educating novice drivers. These findings might have striking implications for accident prevention. This area of study deserves further attention.