The Impact of Environmental Complexity on Drivers’ Situation Awareness

Sam-Min Park, Yilun Xing, K. Akash, Teruhisa Misu, L. Boyle
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引用次数: 3

Abstract

Computational models embedded in advanced driver assistance systems (ADAS) require insights on drivers’ perception and understanding of their environment. This is particularly important as vehicles become increasingly automated and the partnership between the controllers (driver or vehicle) needs to be attentive to each other’s future intentions. This study investigates the impact of environmental factors (road type, lighting) on driver situation awareness (SA) using 75 real-world driving scenes viewed within a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) was adopted to compute SA scores from spatially continuous data. A hurdle model showed that visual complexity, which was not considered in previous SA prediction models, significantly impacted driver SA. The number of objects in the visual scene as well as in the peripheral view were also found to significantly affect driver SA. The findings of this study provide insights on environmental factors that may impact SA predictions.
环境复杂性对驾驶员态势感知的影响
先进驾驶辅助系统(ADAS)中嵌入的计算模型需要了解驾驶员对环境的感知和理解。随着车辆变得越来越自动化,这一点尤为重要,控制器(驾驶员或车辆)之间的合作伙伴关系需要关注彼此的未来意图。本研究通过在驾驶模拟器环境中观察到的75个真实驾驶场景,调查了环境因素(道路类型、照明)对驾驶员态势感知(SA)的影响。采用态势感知全局评估技术(SAGAT)从空间连续数据中计算SA分数。障碍模型表明,视觉复杂性显著影响驾驶员的SA,而以往的SA预测模型并未考虑视觉复杂性。视觉场景中的物体数量以及周边视图中的物体数量也会显著影响驾驶员的SA。本研究的发现为可能影响SA预测的环境因素提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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