CPS: Medium: Collaborative Research: Augmented reality for control of reservation-based intersections with mixed flows

在城市环境中，信号交叉口的实际通行能力很低，是造成交通拥堵的主要原因。自动驾驶汽车是一个可能的飞跃：通过接收来自交叉路口系统本身的协调引导信息，这些车辆可以以最小的减速或等待时间通过交叉路口，从而实现更高效的交叉路口。这些智能交叉路口可以将等待时间减少几个数量级，尽管它们只有在所有车辆都是自动驾驶的情况下才能起作用：即使只有1%的非自动驾驶车辆的存在也会抵消几乎所有的好处。该项目研究增强现实技术作为一种可扩展的手段，通过协调人类驾驶的车辆与自动驾驶车辆，最大限度地提高交叉路口的吞吐量，同时最大限度地减少碰撞风险，来改善这些智能交叉路口的流量。这项研究将通过提供一种廉价、可扩展的方式来减少拥堵，而不需要禁止人类驾驶的交通方式（行人、自行车），也不需要只拥有自动驾驶汽车的成本，从而使美国经济受益。该研究涉及交通工程、控制理论和人因学等多个学科的交叉口，人工驾驶车辆的引导是提高未来智能交叉口安全通行能力的关键。虽然这些交叉口在全自动化的世界中显示出相当大的潜在效益，但即使只有几辆车是人类驾驶的，它们的性能也会大幅下降。考虑到增强现实设备（智能眼镜）的高渗透率，以及来自人类驾驶和自动驾驶车辆的测量数据，包括自动驾驶车辆的预测路径，人类驾驶车辆能否尽可能快速安全地通过智能交叉口？答案需要一个同时解决实时估计和控制问题，在一个动态的环境中，与不确定的驱动人类的表现。该项目开发了有效的算法来学习每个驱动程序的预期性能。在基于预约的交叉口系统中，车辆的路径选择考虑了人的行为和车辆的物理限制。策略的制定，以有效地沟通指导信息的司机在混合现实环境。这些结果将在一个实验装置上进行验证，该装置涉及由人类驾驶并配备增强现实设备的车辆。 该项目是与交通部共同支持的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In urban environments, signalized intersections are a major cause of congestion since their actual capacity is very low. Autonomous vehicles are a possible leap forward: by receiving coordinated guidance information from the intersection system itself, these vehicles could navigate through the intersections with minimal speed reduction or wait times, resulting in far more efficient intersections. These smart intersections can reduce wait times by orders of magnitude, though they only work if all vehicles are autonomous: the presence of even one percent of non-autonomous vehicles would negate almost all benefits. This project investigates augmented reality technology as a scalable means of improving flow through these smart intersections by coordinating human driven vehicles with autonomous vehicles, maximizing intersection throughput while minimizing collision risks. This research will benefit the U.S. economy by providing an inexpensive, scalable way of reducing congestion without the need to ban human-driven forms of transport (pedestrians, bicycles), and without the cost of having only autonomous vehicles. This research is at the interface of several disciplines including transportation engineering, control theory and human factors.The guidance of human-driven vehicles is critical to improve the capacity of future smart intersections safely. While these intersections show considerable potential benefit in a fully automated world, their performance strongly degrades if even a few vehicles are human-driven. Given a high penetration of augmented reality devices (smart glasses), and measurement data from human-driven and autonomous vehicles, including the predicted paths of autonomous vehicles, can human-driven vehicles be guided through a smart intersection as quickly and safely as possible? The answer requires one to simultaneously solve real-time estimation and control problems, in a dynamic environment, with uncertain actuation given the performance of humans. The project develops efficient algorithms to learn the expected performance of each driver. The routing of vehicles in a reservation-based intersection system takes into account human behavior and the physical limitations of vehicles. Strategies are developed to effectively communicate guidance information to drivers in a mixed-reality setting. These results will be validated on an experimental setup involving vehicles driven by humans and equipped with augmented reality devices. This project is jointly supported with the Department of Transportation.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.