Stahl Patrick, Donmez Birsen, Jamieson Greg A
University of Toronto, Department of Mechanical and Industrial Engineering, 5 King's College Road, Toronto, ON M5S 3G8, Canada.
Accid Anal Prev. 2016 Jun;91:103-13. doi: 10.1016/j.aap.2016.02.030. Epub 2016 Mar 11.
This paper evaluates two different types of in-vehicle interfaces to support anticipation in driving: one aids attention allocation and the other aids interpretation of traffic in addition to attention allocation.
Anticipation is a competency that has been shown to facilitate safety and eco-driving through the efficient positioning of a vehicle for probable, upcoming changes in traffic. This competency has been shown to improve with driving experience. In an earlier simulator study, we showed that compared to novice drivers, experienced drivers exhibited a greater number of timely actions to avoid upcoming traffic conflicts. In this study, we seek to facilitate anticipation in general and for novice drivers in particular, who appear to lack the competency. We hypothesize that anticipation depends on two major steps and that it can be supported by aiding each: (1) conscious perception of relevant cues, and (2) effective processing of these cues to create a situational assessment as a basis for anticipation of future developments.
We conducted a simulator experiment with 24 experienced and 24 novice drivers to evaluate two interfaces that were designed to aid the two hypothesized steps of anticipation. The attentional interface was designed to direct attention toward the most relevant cue. The interpretational interface represented several cues, and in addition to directing attention also aimed to aid sense-making of these cues.
The results confirmed our hypothesis that novice drivers' anticipation performance, as measured through timely actions to avoid upcoming traffic conflicts, would be improved with either interface type. However, results contradicted our expectation that novice drivers would obtain larger improvements with the interpretational interface. Experienced drivers performed better than novice drivers to begin with and did not show any statistically significant improvements with either interface.
Both interfaces improved anticipation performance for novice drivers. Future research should evaluate the effectiveness of these interfaces in a wider variety of driving conditions, such as when the driver is multitasking.
本文评估两种不同类型的车载界面,以支持驾驶中的预判能力:一种有助于注意力分配,另一种除了有助于注意力分配外,还能辅助对交通情况的解读。
预判是一种能力,已证明通过有效定位车辆以应对可能出现的交通变化,有助于提高安全性和实现生态驾驶。这种能力会随着驾驶经验的增加而提高。在早期的模拟器研究中,我们发现与新手司机相比,经验丰富的司机为避免即将发生的交通冲突而采取的及时行动更多。在本研究中,我们试图总体上促进预判能力,特别是针对似乎缺乏这种能力的新手司机。我们假设预判取决于两个主要步骤,并且可以通过辅助每个步骤来提供支持:(1)对相关线索的有意识感知,以及(2)对这些线索进行有效处理,以创建情境评估作为预判未来发展的基础。
我们对24名经验丰富的司机和24名新手司机进行了模拟器实验,以评估旨在辅助预判的两个假设步骤的两种界面。注意力界面旨在将注意力引向最相关的线索。解读界面呈现多个线索,除了引导注意力外,还旨在辅助对这些线索的理解。
结果证实了我们的假设,即通过避免即将发生的交通冲突的及时行动来衡量,新手司机的预判表现会因任何一种界面类型而得到改善。然而,结果与我们的预期相矛盾,即新手司机使用解读界面会获得更大的改善。经验丰富的司机一开始表现就比新手司机好,并且使用任何一种界面都没有显示出任何统计学上的显著改善。
两种界面都提高了新手司机的预判表现。未来的研究应该在更广泛的驾驶条件下评估这些界面的有效性,例如当司机进行多任务操作时。