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

在城市环境中，信号交叉口是造成拥堵的主要原因，因为它们的实际容量非常低。自动驾驶汽车可能是一个飞跃：通过接收来自十字路口系统本身的协调引导信息，这些车辆可以以最小的速度降低或等待时间通过十字路口，从而实现更高效的十字路口。这些智能十字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有效：即使只有1%的非自动驾驶车辆存在，也会抵消几乎所有的好处。该项目将增强现实技术作为一种可扩展的手段，通过协调人类驾驶车辆和自动驾驶车辆，最大限度地提高路口吞吐量，同时最大限度地降低碰撞风险，改善这些智能路口的交通。这项研究将通过提供一种廉价、可扩展的方式来减少交通拥堵，而不需要禁止人类驱动的交通方式（行人、自行车），也不需要只有自动驾驶汽车的成本，从而使美国经济受益。本研究是交通工程、控制理论和人为因素等多学科的交叉研究。人类驾驶车辆的引导对于提高未来智能交叉口的安全通行能力至关重要。虽然这些十字路口在完全自动化的世界中显示出相当大的潜在优势，但如果只有少数车辆是人工驾驶的，它们的性能就会严重下降。考虑到增强现实设备（智能眼镜）的高度普及，以及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，人类驾驶的车辆能否尽可能快速安全地通过智能十字路口？答案需要在动态环境中同时解决实时估计和控制问题，在给定人类行为的不确定驱动下。该项目开发了高效的算法来学习每个驾驶员的预期性能。在基于预留的交叉口系统中，车辆的路径选择考虑了人的行为和车辆的物理限制。开发了在混合现实环境中有效地向驾驶员传达引导信息的策略。这些结果将在配备增强现实设备的人类驾驶车辆的实验装置上得到验证。这个项目是与交通部共同支持的。该奖项反映了美国国家科学基金会的法定使命，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.