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

CPS：中：协作研究：协同作用：用于控制具有混合自主-非自主流的基于预留的交叉口的增强现实

• 批准号: 1739964
• 负责人: Christian Claudel
• 金额: $ 62.16万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739964&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Synergy

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.

在城市环境中，信号交叉口是造成拥堵的主要原因，因为它们的实际通行能力非常低。自动驾驶车辆可能是一次飞跃：通过从十字路口系统本身接收协调的引导信息，这些车辆可以以最小的减速或等待时间导航通过十字路口，从而使十字路口更加高效。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能发挥作用：即使是百分之一的非自动驾驶车辆的存在也会抵消几乎所有好处。该项目研究增强现实技术作为一种可扩展的手段，通过协调人类驾驶车辆与自动驾驶车辆来改善这些智能十字路口的流量，最大限度地提高十字路口吞吐量，同时最大限度地减少碰撞风险。这项研究将通过提供一种廉价、可扩展的方式来减少拥堵，从而使美国经济受益，而无需禁止人类驱动的交通方式（行人、自行车），也无需支付仅拥有自动驾驶车辆的成本。这项研究涉及交通工程、控制理论和人为因素等多个学科的交叉点。人类驾驶车辆的引导对于提高未来智能十字路口的安全能力至关重要。虽然这些交叉路口在完全自动化的世界中显示出相当大的潜在好处，但如果即使只有少数车辆是人工驾驶，它们的性能也会大幅下降。鉴于增强现实设备（智能眼镜）的高度普及，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，是否可以引导人类驾驶车辆尽可能快速、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，考虑到人类的表现，驱动具有不确定性。该项目开发了有效的算法来了解每个驾驶员的预期性能。基于预订的交叉路口系统中的车辆路线考虑了人类行为和车辆的物理限制。制定策略是为了在混合现实环境中有效地向驾驶员传达指导信息。这些结果将在涉及人类驾驶并配备增强现实设备的车辆的实验装置上得到验证。 该项目由美国交通部共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

使用凸优化进行可扩展且鲁棒的交通密度估计的控制理论方法

• DOI: 10.1109/tits.2019.2953023
• 发表时间: 2018-12-05
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Sebastian A. Nugroho;A. Taha;C. Claudel
• 通讯作者: C. Claudel

Parallelized Active Information Gathering Using Multisensor Network for Environment Monitoring

使用多传感器网络并行主动信息收集进行环境监测

• DOI: 10.1109/tcst.2021.3069175
• 发表时间: 2021-04-30
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Bin Du;Kun Qian;C. Claudel;Dengfeng Sun
• 通讯作者: Dengfeng Sun

Jacobi-style iteration for distributed submodular maximization

用于分布式子模最大化的雅可比式迭代

• 发表时间: 2022-06
• 期刊: IEEE transactions on automatic control
• 影响因子: 6.8
• 作者: Bin Du; Kun Qian
• 通讯作者: Kun Qian

Real-time Mobile Sensor Management Framework for city-scale environmental monitoring

用于城市规模环境监测的实时移动传感器管理框架

• DOI: 10.1016/j.jocs.2020.101205
• 发表时间: 2020-10
• 期刊: Journal of computational science
• 作者: Qian, Kun;Claudel, Christian
• 通讯作者: Claudel, Christian

HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data

HAR-GCNN：用于根据高度未标记的移动传感器数据进行人类活动识别的深度图 CNN

• 发表时间: 2022-03
• 期刊: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops
• 作者: Abduallah Mohamed; Fernando Lejarza
• 通讯作者: Fernando Lejarza