Fleeting Decisions and Risks in Pedestrian Road-Crossing Behavior: Building Insight with Next-Generation Data, Models, and Platforms

在这个项目中，首席研究员将通过观察和研究现实世界的过马路地点，在非常小的空间和时间内检查过马路的行为。观察到的条件将在一个动作捕捉工作室中重现，这将允许收集非常详细的数据，可以用来建立训练，规划和设计的假设模型。决定过马路可能只需要几分之一秒和几厘米的动作，但在这短暂的时间和空间窗口内，人们在与车辆的碰撞中面临着受伤和死亡的风险。尽管几十年来一直在努力设计可以促进安全过马路行为的干预措施，但许多人继续从事不安全的过马路行为，其中包括我们最脆弱的行人——年轻人和老年人。该项目的目的是揭示人们在路边移动时经常出现的草率决定和风险。对过马路的微观世界的关注将使识别和测量来自肢体语言、表情、人群中的社会线索、凝视行为和脚步的微妙信号成为可能，这些信号可以传达对过马路意图的隐蔽和公开的洞察。这些信号和与之相关的过马路行为将有助于对过马路活动的可行理解，这些活动可用于改进过马路基础设施的设计，可用于培训和教育，可用于增强信号方案，可用于通知目前部署在许多车辆上的高级驾驶员感知系统。该项目利用一种耦合研究仪器，将编码的真实世界观察与运动捕捉工作室中基于先进传感器的仪器相结合，并与基于人类自动机的假设建模相结合，从而促进探索、识别和测量行人在过马路时做出决策和冒险的微妙信号。该项目的技术重点是识别、捕获、测量和构建行人过马路行为的人-环境信号，这些信号可用于更广泛的行人感知和检测。该项目将生成一个强大的数据库，包括定性观察、传感器数据和模型输出，可被社会行为科学和计算科学社区重用。这些数据，以及它们将支持的模型，将构成一个沉浸式虚拟地理环境的主干，可用于培训和教育。

In this project, the Principal Investigator will examine road-crossing behavior over very small moments of space and time, by observing and studying real-world road-crossing sites. The conditions observed will then be recreated in a motion capture studio that will allow collection of very detailed data that can be used to build what-if models for training, planning, and design. The decision to cross a road can take mere fractions of a second and centimeters of action, but over this small window of space and time, people are at risk of injury and death in collisions with vehicles. Despite decades of work to design interventions that might promote safe crossing behavior, many people continue to engage in unsafe crossing, among them our most vulnerable - pedestrian youth and seniors. The aim of the project is to uncover the, often cursory, decisions and risks that people invoke when they move at the roadside. The focus on the microcosm of road-crossing will enable identification and measurement of subtle signals from body language, expression, social cues in crowds, gaze behavior, and footfall that can convey both covert and overt insight into crossing intent. These signals and the road-crossing behaviors that they relate to will inform actionable understanding of road-crossing activities that can be used to improve the design of crossing infrastructure, that can be used in training and education, that might enhance signaling schemes, and that can be used to inform Advanced Driver Awareness Systems now being deployed in many vehicles.The project make use of a coupled research instrument that allows for coded real-world observation to be paired with advanced sensor-based instrumentation in motion capture studios, and with what-if modeling based on human automata that can facilitate the exploration, identification, and measurement of subtle signals of pedestrian decision-making and risk-taking while crossing. The technical focus of the project is to identify, capture, measure, and frame human-environment signals of road-crossing behavior in ways that can be used for broader pedestrian sensing and detection. The project will generate a robust library of data, including qualitative observations, sensor data, and model output that can be reused by communities in the socio-behavioral sciences and the computing sciences. These data, and the models they will power, will form the backbone of an immersive Virtual Geographic Environment that can be used for training and education.