Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, are low-cost compared to on-road or naturalistic studies and allow researchers to fully control variables of interest. In this paper, we propose a framework ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.
翻译:为更好地为弱势道路使用者,特别是自行车使用者和行人提供设计信息,我们需要开展更多的研究,评估双骑者和行人的行为和生理状况如何改变不同的道路设计和背景环境。以前的研究突出地说明了模拟虚拟环境在进行双骑者和行人研究方面的优势。这些环境不使参与者面临受伤风险,与地面或自然研究或自然研究相比成本低,并使研究人员能够充分控制感兴趣的变数。在本文中,我们提议一个ORCLSim框架,以支持IVE内的人感测技术,以评价不同背景环境中的双骑者和行人生理和行为变化。为了展示这一框架,我们提出两个案例研究,我们收集和分析5名参与者在双骑人和行人研究环境中的生理和行为反应试点数据,代表真实世界道路路段和交通条件。这些案例研究的结果表明,生理数据对道路环境的变化和实时事件,特别是心脏率和凝视行为的变化十分敏感。此外,我们的初步数据表明,参与者对不同背景环境的生理变化可能做出不同反应。通过不同程度的模型分析,这些变化会影响到参与者。