增强现实平视显示(即AR-HUD),能够通过实时计算为用户提供准确、有效的AR预警信息,从智能驾驶辅助的角度帮助用户防范各类驾驶风险。当前AR-HUD可显示的信息逐渐增多,这可能会导致驾驶员的视觉感知负荷增加。本研究以行人预警为切入点,...增强现实平视显示(即AR-HUD),能够通过实时计算为用户提供准确、有效的AR预警信息,从智能驾驶辅助的角度帮助用户防范各类驾驶风险。当前AR-HUD可显示的信息逐渐增多,这可能会导致驾驶员的视觉感知负荷增加。本研究以行人预警为切入点,采用2 (有无AR增强提示,被试间) × 2 (拥挤程度,被试内) × 2 (行人位置,被试内)三因素混合实验设计,基于模拟驾驶视频,探索AR-HUD预警显示中伴随的视觉复杂度对驾驶员感知拥挤程度场景中的不同位置的意外刺激时的视觉注意和行为绩效的影响。主要研究结论有:1) 有AR增强提示下的驾驶员会更快感知到意外行人刺激,并更快做出反应。2) 驾驶员会对拥挤条件下的意外行人做出更快的反应,AR预警可以提高该状态下的视觉搜索效率。Augmented Reality Head-up Display (also known as AR-HUD) can provide users with accurate and effective AR warning information through real-time calculations, and help users prevent all kinds of driving risks from the point of view of intelligent driving assistance. The current AR-HUD can display more and more information, which may lead to an increase in the driver’s visual perceptual load. Taking pedestrian warning as an entry point, this study adopts a 2 (with or without AR-enhanced cues, between subjects) × 2 (congestion level, within subjects) × 2 (pedestrian location, within subjects) three-factor mixed experimental design based on a simulated driving video to explore the visual complexity accompanying AR-HUD warning displays on drivers' visual attention and behavioral performance when perceiving unexpected stimuli in different locations in congestion level scenarios. The main findings are: 1) Drivers with AR cues will perceive and respond to unexpected pedestrian stimuli faster. 2) Drivers will respond faster to unexpected pedestrians in crowded conditions, and AR warnings can improve visual search efficiency in this state.展开更多
文摘增强现实平视显示(即AR-HUD),能够通过实时计算为用户提供准确、有效的AR预警信息,从智能驾驶辅助的角度帮助用户防范各类驾驶风险。当前AR-HUD可显示的信息逐渐增多,这可能会导致驾驶员的视觉感知负荷增加。本研究以行人预警为切入点,采用2 (有无AR增强提示,被试间) × 2 (拥挤程度,被试内) × 2 (行人位置,被试内)三因素混合实验设计,基于模拟驾驶视频,探索AR-HUD预警显示中伴随的视觉复杂度对驾驶员感知拥挤程度场景中的不同位置的意外刺激时的视觉注意和行为绩效的影响。主要研究结论有:1) 有AR增强提示下的驾驶员会更快感知到意外行人刺激,并更快做出反应。2) 驾驶员会对拥挤条件下的意外行人做出更快的反应,AR预警可以提高该状态下的视觉搜索效率。Augmented Reality Head-up Display (also known as AR-HUD) can provide users with accurate and effective AR warning information through real-time calculations, and help users prevent all kinds of driving risks from the point of view of intelligent driving assistance. The current AR-HUD can display more and more information, which may lead to an increase in the driver’s visual perceptual load. Taking pedestrian warning as an entry point, this study adopts a 2 (with or without AR-enhanced cues, between subjects) × 2 (congestion level, within subjects) × 2 (pedestrian location, within subjects) three-factor mixed experimental design based on a simulated driving video to explore the visual complexity accompanying AR-HUD warning displays on drivers' visual attention and behavioral performance when perceiving unexpected stimuli in different locations in congestion level scenarios. The main findings are: 1) Drivers with AR cues will perceive and respond to unexpected pedestrian stimuli faster. 2) Drivers will respond faster to unexpected pedestrians in crowded conditions, and AR warnings can improve visual search efficiency in this state.
