EAGER: SAI: Human-Centered Design and Enhancement of Next Generation Transportation Infrastructure with Connected and Automated Vehicles

加强美国基础设施(SAI)是一项NSF计划，旨在刺激以人类为中心的基础性研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，增长经济，创造就业机会，提高公共部门服务提供效率，加强社区力量，促进机会均等，保护自然环境，增强国家安全，推动美国的领导地位。要实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策、治理以及社会和文化过程的知识如何能够建立和维护有效的基础设施，改善生活和社会，并建立在技术和工程进步的基础上。自动驾驶车辆，或连接和自动车辆(CAV)，被倡导作为提高交通系统安全的解决方案。然而，目前的交通基础设施只为人类驾驶员设计，没有考虑自动驾驶车辆的特点或它们与其他自动驾驶和人类驾驶车辆(HDV)的相互作用。当两种车辆并存在道路上时，自动驾驶和人类驾驶车辆之间的沟通错误和不当互动可能会导致过渡期发生更多事故。尽管交通基础设施的适应与车辆的技术进步一样关键，但大多数关于无人驾驶和人工驾驶车辆交通的研究都忽视了交通基础设施的作用。该项目旨在通过研究未来基础设施设计方法来加强美国的交通基础设施，这些基础设施设计方法支持自动驾驶车辆、基础设施和人类驾驶车辆之间的通信，以提高自动驾驶汽车的安全性和速度。未来的“智能”交通基础设施必须支持互联和自动化车辆(CAV)之间以及CAV和人类驾驶车辆(HDV)之间的通信，才能实现高效和相对无错误的车辆运输目标。该项目旨在通过将人类的认知和行为与系统方法相结合来促进基础设施设计方面的知识，将道路运输视为一个由CAV、人驾驶车辆和基础设施子系统组成的整体系统。该项目的中心假设是，根据CAV的新功能进行优化的交通基础设施将显著减少事故和交通延误。这些方法采用了以人为中心的设计框架，重点关注HDV操作员与CAV互动的感知和行动。目的是建立一个能够指导未来混合交通条件下车辆运输系统设计的实证研究基地。为此，多学科调查团队将1)从事故报告、对CAV专家的采访、对司机的调查以及对驾驶模拟器中人类认知和行为的研究中找出HDV和CAV之间事故的根本原因；2)根据已确定的根本原因、信息需求和人类信息处理提出对策解决方案，以向人类驾驶员和CAV提供必要的信息；以及3)使用对策解决方案和信息需求来了解当前基础设施的改进，以更好地支持HDV和CAV之间的沟通和互动。作为最后一步，PIS将在现实世界的环形交叉路口评估建议的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Self-driving vehicles, or connected and automated vehicles (CAVs), are advocated as a solution to improve the safety of the transportation system. However, current transportation infrastructure is only designed for human drivers, without considering the characteristics of self-driving vehicles or their interactions with other self-driving and human-driven vehicles (HDVs). Miscommunication and improper interactions between self-driving and human-driven vehicles may lead to more accidents during the transition period when the two vehicle types coexist on the roadway. Despite the fact that adaptations of transportation infrastructure are as critical as the technological advances of the vehicles, most research on traffic with both driverless and human-driven vehicles has disregarded the role of transportation infrastructure. This project seeks to strengthen American transportation infrastructure by investigating future infrastructure design methods that support communication among self-driving vehicles, infrastructure, and human-driven vehicles to enhance safety and speed widespread adoption of self-driving vehicles.The “smart” transportation infrastructure of the future must support communication among connected and automated vehicles (CAVs) and between CAVs and human-driven vehicles (HDVs) if the goal of efficient and relatively error-free vehicle transportation is to be attained. This project aims to advance knowledge in infrastructure design by integrating the cognition and actions of humans with a system-of-systems approach, viewing roadway transportation as an overall system consisting of CAVs, human-driven vehicles, and infrastructure subsystems. The central hypothesis of this project is that transportation infrastructure that is optimized based on the new features of CAVs will appreciably reduce accidents and traffic delays. The methods take a human-centered design framework that focuses on the perceptions and actions of HDV operators in relation to interactions with CAVs. The goal is to develop an empirical research base that will guide the future vehicle transportation system design under mixed traffic conditions. To this end, the multidisciplinary team of investigators will 1) Identify root causes of accidents between HDVs and CAVs from accident reports, interviews of CAV experts, surveys of drivers, and studies of human cognition and actions in a driving simulator; 2) Propose countermeasure solutions to deliver necessary information to human drivers and CAVs based on identified root causes, information needs, and human-information processing; and 3) Use the countermeasure solutions and information needs to understand improvements in current infrastructure that would better support communication and interactions between HDVs and CAVs. As a final step, the PIs will evaluate the proposed solutions at a real-world roundabout.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.