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的时刻检查过马路行为。然后，观察到的条件将在运动捕捉工作室中重新创建，这将允许收集非常详细的数据，这些数据可用于构建用于培训，规划和设计的假设模型。过马路的决定可能只需要几分之一秒和几厘米的行动，但在这狭小的空间和时间窗口内，人们面临着与车辆碰撞造成伤亡的风险。尽管几十年来一直在努力设计可能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，其中包括我们最脆弱的行人青年和老年人。该项目的目的是揭示人们在路边移动时所做的往往是粗略的决定和风险。对过马路微观世界的关注将使识别和测量来自肢体语言、表情、人群中的社交线索、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。这些信号及其相关的过马路行为将为对过马路活动的可操作理解提供信息，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，可能会增强信号方案，这可以用来通知先进的驾驶员意识系统，现在正在部署在许多车辆。该项目利用耦合的研究仪器，允许编码的真实世界观察与运动捕捉工作室中先进的基于传感器的仪器配对，并与基于人类自动机的假设模型配对，可以促进探索，识别和测量行人决策和冒险的微妙信号。该项目的技术重点是识别，捕获，测量和构建道路交叉行为的人-环境信号，以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，可供社会行为科学和计算科学社区重用。这些数据及其支持的模型将构成可用于培训和教育的沉浸式虚拟地理环境的支柱。

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.