HCC: Small: Modeling the Validity and Transfer of Eye-Scanning Patterns for Hazard Perception from Virtual Reality Training Environments to Reality

HCC：小型：对从虚拟现实训练环境到现实的危险感知的眼睛扫描模式的有效性和迁移进行建模

• 批准号: 1116378
• 负责人: Nicholas Ward
• 金额: $ 49.96万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116378&HistoricalAwards=false
• 关键词: HCC; Small; Modeling; Validity; Transfer

Visual search skills for hazard perception are critical in many domains. They are used by pilots to maintain situation awareness, by doctors reviewing screen images to diagnose health disorders, and by security screeners inspecting for hazardous materials. They are also critical to a novice driver's ability to detect roadway hazards. For novice drivers, poor visual search skills can increase the risk for traffic fatalities, which are the leading cause of death for teenagers nationwide. Virtual reality can be used to provide visual search training during driver education. However, the success of these training programs depends on the validity of eye-scanning patterns in the virtual environment and on the simulation parameters employed to elicit those patterns. The overall objective of the research is to assess the validity of search patterns in the virtual world as they apply to the physical realm, and then to identify which simulation parameters are critical for successful simulator-based hazard perception training. Intellectual merit: The project will integrate theory and data to advance our knowledge of which parameters of virtual reality-based hazard perception training promote the greatest transfer of training to real world driving. Specifically, the project will (a) examine the relationship between visual search in driving simulators vs. that in the real world, using specially equipped vehicles; (b) test which aspects of the driving simulator lead to the best training and transfer of learning; and (c) examine how age and experience influence hazard perception skills. Broader impacts: The findings from the project will advance scientific understanding of how virtual reality training simulations can be used to improve driver education, thereby leading to better drivers and fewer traffic accidents. The results will also be applicable to other areas in which virtual reality training can be valuable, such as medical diagnosis and security screening. In addition, the project will enable a number of disadvantaged students to take part in engineering and technology research and includes a high school outreach component to engage students in research at an early age.

视觉搜索技能的危险感知是至关重要的，在许多领域。它们被飞行员用来保持态势感知，被医生用来检查屏幕图像以诊断健康问题，以及被安全检查人员用来检查危险材料。它们对新手驾驶员检测道路危险的能力也至关重要。对于新手司机来说，糟糕的视觉搜索技能会增加交通死亡的风险，这是全国青少年死亡的主要原因。虚拟现实可用于在驾驶员教育期间提供视觉搜索训练。然而，这些培训计划的成功取决于在虚拟环境中的眼睛扫描模式的有效性和模拟参数，以引出这些模式。 研究的总体目标是评估搜索模式在虚拟世界中的有效性，因为它们适用于物理领域，然后确定哪些模拟参数是成功的基于模拟器的危险感知训练的关键。智力优点：该项目将整合理论和数据，以推进我们对基于虚拟现实的危险感知训练的哪些参数能最大程度地促进训练向真实的世界驾驶的转移的认识。 具体而言，该项目将（a）研究驾驶模拟器中的视觉搜索与使用特殊装备的车辆在真实的世界中的视觉搜索之间的关系;（B）测试驾驶模拟器的哪些方面导致最佳培训和学习转移;（c）研究年龄和经验如何影响危险感知技能。 更广泛的影响：该项目的研究结果将促进对虚拟现实培训模拟如何用于改善驾驶员教育的科学理解，从而提高驾驶员的驾驶能力，减少交通事故。 研究结果也将适用于虚拟现实培训可能有价值的其他领域，例如医疗诊断和安全检查。此外，该项目将使一些处境不利的学生能够参加工程和技术研究，并包括一个高中外联部分，使学生在幼年时就参与研究。